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Review o Cognitive Linguistics : (), –. ./rcl...kim

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Optimizing the analysis of metaphorin discourse

How to make the most o qualitative sofware

and find a good research design

Michael KimmelUniversity o Vienna

Tis article presents a sofware-based methodology or studying metaphor

in discourse, mainly within the ramework o Conceptual Metaphor Teory

(CM). Despite a welcome recent swing towards methodological reflexivity, a

detailed explication o the pros and cons o different procedures is still in order

as ar as qualitative research (i.e. a context-sensitive manual coding o a text

corpus) is concerned. Qualitatively oriented scholars have to make difficultdecisions revolving around the general research design, the transer o linguis-

tic theory into method, good workflow management, and the aimed at scope

o analysis. My first task is to pinpoint typical tasks and demonstrate how they

are optimally dealt with by using qualitative annotation sofware like ALAS.ti.

Sofware not only streamlines metaphor tagging itsel, it systematizes the inter-

pretive work rom grouping text items into systematic/conceptual metaphor sets,

via data surveys and checks, to quantitative comparisons and a cohesion-based

analysis. My second task is to illustrate how a good research design can provide

a step-wise procedure, offer systematic validation checks, keep the code systemslim and many analytic options open. When we aim at complex data searches

and want to handle high metaphor diversity I recommend compositional coding ,

i.e. tagging source and target domains separately (instead o adopting a “one

mapping-one code” strategy). Furthermore, by tagging metaphors or image-

schematic and rich semantic source domains in parallel, i.e. two-tier coding , we

get multiple options or grouping metaphors into systematic sets.

Keywords: metaphor analysis, qualitative methods, sofware assisted analysis,

research design and workflow management, data stratification, EU discourse


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Michael Kimmel

. Qualitative research on metaphor in discourse

Conceptual Metaphor Teory (CM) afer George Lakoff and Mark Johnson(1980, 1999) has had a revolutionary impact in recent years, and, afer some delay,

in the past ten years has led to a surge o metaphor-based discourse analysis. CM

provides an account notable or the scope o metaphoric phenomena and applied

fields studied, its analysis o the role o “conceptual metaphors” in motivating lin-

guistic, gestural, and visual expressions, and its analysis o imagery and abstract

conceptual structure that underlies metaphors. CM has proven its practical value

in hundreds o linguistic and several dozens o discourse studies and, despite some

imperections, enjoys its popularity or a good reason. It strikes a healthy balance

between descriptive adequacy on the one hand and ease o application on the

other (or example when compared to the nuanced, but also more cumbersome

and less intuitive Blending Model o metaphor afer Fauconnier & urner, 2002).

Discourse research benefits rom the twin acts that CM can be straightorwardly

operationalized as a social science methodology and that a wealth o existing stud-

ies inorm prospective researchers about typical metaphor patterns.

In the wake o the seminal volume Researching and Applying Metaphor edited

by Lynne Cameron and Graham Low (1999) the need or sound methodology has

come to be generally realized. However, despite many advances, many present-dayscholars lack practical orientation, especially when it comes to thoroughly quali-

tative (i.e. comprehensive, context-inormed, and relatively data-driven) studies.

As Schmitt (2005, p. 369) states “the systematic analysis o metaphor, as a herme-

neutic process, remains an applied art. Te reconstruction o metaphorical mod-

els cannot be automated; the process can only be learned.” But how is this done?

A first common concern is to translate the rich theoretical background o CM

into the logic o qualitative research. In implementing a study researchers need

to understand which research aim necessitates which strategy, e.g. how corpus

size and the desired analytic granularity figure in the equation. Tey also need to

know about characteristic pitalls, required compromises, and what makes or a

good workflow. Furthermore, researchers need validation checks (c. Low & odd,

2010), benchmarks or basic analytic techniques, as well as add-ons or special

needs. A final widespread problem is the efficient administration o large projects

and team-work.

I shall begin this paper with a review o the methodology debate and com-

mon tasks in metaphor research (this section). Section 2 then presents a strategy

o sofware use, taking care to hold apart the scholar’s theory inormed skills romwhat the tool does. Section 3 illustrates how tabular sofware output makes or a

structured scientific write-up, while keeping its moorings in the data transpar-

ent. Section 4 then moves into a meta-reflection o research designs and explores


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Optimizing the analysis o metaphor in discourse

the analytical scope gained through well-calibrated sofware. Troughout, I shall

mainly draw on examples rom a study o my own on British EU discourse.

. Methodological and procedural standards

Where do we presently stand in the methodology debate? On the asset side, ma-

jor inroads have been made to clariy the intricacies o metaphor identification

(Pragglejaz Group, 2007), to sketch research logic in general (Steen, 1999), to

provide guidelines or discourse research (Cameron, 2003; Schmitt, 2005) and to

discuss the challenges in more specific kinds o approaches (Cameron & Maslen,

2010a). Te advent o the computer has considerably contributed to systematicity

and scope. Tis begins with classic corpus linguistic tools or searching large cor-

pora (e.g., Deignan, 2005; Musolff, 2004; Charteris-Black, 2004), goes via semantic

tools (e.g., Koller et al., 2008), and ends with databases like MEALUDE (Goatly,

2007) or the Hamburg Metaphor Database that compile metaphors or later search

by conceptual types and/or lexis.1 Other research has been dedicated to the discur-

sive specifics o metaphor, based on manually coded and closely analyzed smaller

corpora (e.g., Schmitt, 1995). Tis may involve the use o commercially available

qualitative annotation tools (Gugutzer, 2002, p. 156ff) or purpose-tailored tools

such as VisDis (Cameron & Stelma, 2004). As a net outcome, discourse approach-es have rectified too narrow or over-generalizing claims that have dominated

cognitive linguistics or some time. First and unsurprisingly, a broader range o

metaphors than first posited by “armchair” linguists has been documented. Te

systematic approach o the discourse paradigm corrects against researcher bias in

choosing metaphors, e.g. by ollowing one’s routine reading habits. Second, sev-

eral assumed metaphor patterns have been ound to be insufficiently systematic in

language or not ormulated at the right level o abstraction (Goatly, 2007). Tis has

created some awareness o the dangers inherent in postulations o single “central”

or “organizing” metaphors. Tird, it has come to be recognized that some meta-

phors exhibit systematicity in specific, time-bound discourses only (Zinken et al.,

2008). Finally, the move rom cherrypicking to comprehensive metaphor coding

is making new theoretical questions tractable. Some large studies aim to trace dis-

course dynamics (Liebert, 1997; Cameron, 2010). Others reveal how discourses

create opinion diversity within a limited scope o metaphor (Musolff, 2004), how

conceptual models constituted via metaphor complement each other (Kimmel,

2009a), or how metaphor fields eed into higher-level, but logically orthogonal

cultural themes (Quinn, 1991).On the debit side, the problems o doing metaphor analysis have been dis-

cussed only in the very recent past. Researching and teaching has made me acutely

aware o the pitalls and strategic decisions that metaphor scholars ace. Yet, most


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Michael Kimmel

publications keep the inevitable stumbling blocks in the dark and the applied ra-

tionales o coping to a minimum. It is common (and perhaps unavoidable) in aca-

demic writing to iron out the smaller creases and kinks o the research processto present a more coherent picture. Difficulties are given short shrif, instead o

publishing project notes others may learn rom. Such problems may concern the

sampling, the elicitation procedure (when interviews or ocus groups are used),

the reliability o coder ratings, the disentangling o metaphor rom related phe-

nomena, and the grouping metaphors into coherent sets, amongst others (c. Low

& odd, 2010). Furthermore, studies requently ail to make their data generation

process transparent enough. Hardly an article presents a corpus in its ull original

complexity or talks about its metaphorically less systematic parts. Tis makes us

lose sight o how the coherence we see in a publication is inevitably also an out-

come o the researcher’s interpretive endeavor and choice o material. By a similar

token, quantitative aspects o a corpus such as metaphor requency, diversity (in

general and by target), and type/token ratio are not discussed or their implications

(or even mentioned) by many authors. Next, meta-reflexive evaluations o how

suitable metaphor analysis is or a given topic remain largely absent, at least to the

extent that comparisons with other methods are rare. Finally, issues o study de-

sign are given little explicit attention. Even well-versed researchers have to decide

technical issues, choose a level o granularity, and deploy their time-resources inaccordance with the scope o their aims. Alternative strategies are hardly ever con-

trasted, in order to give prospective researchers criteria or deciding what will suit

their needs. Te root common to all o these shortcomings is that comprehensive

procedural standards or qualitative metaphor research are slow to come. I meta-

phor scholarship is to measure up against the best practice o qualitative research

at large it needs to emulate their explicit procedures, as well as being transparent

about the way theorizing is rooted in the data. Hence, a systematic approach should

– guide scholars through a project step-by-step in their endeavor to reconstruct(a) conceptual models or (b) discourse dynamics through a collection, catego-

rization, and analysis o metaphors in a corpus,

– provide checks and keep the moorings o the analysis in the data backwards

traceable (c. “audit trail”, Cameron & Maslen, 2010b, p. 99), and

– reflexively explicate all strategic choices and the possible bias that results rom it.

Te aorementioned hermeneutic nature o the applied art o metaphor analysis is

certainly not a license or an “anything goes” or reliance on intuition. It is because

the qualitative research community recognizes interpretive expertise as somethingirreducible that readers o a study should be able to reconstruct how claims came

about.


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. Te general steps o metaphor research

Let us assume we have assembled a corpus (o interview transcripts, newspaper

texts, etc.) or a study and want to proceed in steps. Which tasks comprise the skill

o analyzing metaphoric discourse?

(1) Delimiting the target area o interest according to the research question; de-

ciding whether all or only some metaphors are coded.

(2) Identiying metaphor units in discourse (which includes deciding on border-

line cases and deciding on one’s cut-off principles); a parallel task is deciding

on the maximum text span or metaphor units.

(3) Grouping metaphors into sets o conceptually similar tokens; finding a or-

mula or them; reconstructing metaphor coherence.

(4) Analyzing the thematic relevance o each set and its discourse unctions (eval-

uation, highlighting, inerences, etc.).

(5) Counting metaphor incidence and diversity within a corpus; comparing num-

bers across media (e.g. several newspapers or interviews) or across time (e.g.

beore and afer a therapy).

(6) Reconstructing textual metaphor cohesion (e.g. clustering or not).

Virtually all empirical metaphor research engages in the first three o these steps.

Depending on their research questions and their aimed at depth, scholars may add

some or all o the optional steps iv–vi.

Delimiting the target domain(s) o interest . Afer selecting the material or the

corpus, the researcher’s first task is to delineate her field and decide “do I study

metaphors on all topics [= target domains] or only some?” Some studies endeavor

to capture metaphors o whatever target domain, or instance because they take

metaphor density in the broadest sense as an indicator o “hot spots” in discourse

(Cameron & Stelma, 2004). With this strategy targets are discovered in an exclu-sively bottom-up ashion. More typically however, a restriction to one or a small

set o related domains makes sense, because the researcher wants to maintain a

thematic ocus. Tis means discarding all off-topic metaphors without interest to

the study. Te act that theme-unspecific metaphors usually come in high num-

bers requires us to clearly delimit the admissible target(s). For example, in study-

ing the conceptualization o European integration (see Section 3), politics unre-

lated metaphors like “in a state o renzy”, “it was striking”, “I elt that we should do

it”, or “one could see that they had gone wrong” were discarded because they relate

to targets that one would probably find anywhere and that reveal little about the

topic. More problematically, I had to decide i only metaphors or the target EU

[i.e. “the EU is X”] itsel qualiy or i various processes related to the EU should


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Michael Kimmel

be included. In the end I decided that all these as well as targets like “politicians”

and “political action” would benefit the study. Finally, one may also decide to ex-

clude certain general types o metaphors like ontological metaphors (see 2.4) andpersonifications.

Metaphor identification and unit size. Next, the scholar’s contextually and theoreti-

cally inormed skills are needed to identiy expressions that maniest the linguis-

tic category o metaphor. ypically, one careully reads a text looking or vehicle-

words that signal a metaphor. Te context is important here. In a sentence relating

to political institutions the word “architecture” signals a metaphor, whereas in

a context o urban planning it will probably be literal. Deciding what qualifies

as a metaphor is by no means trivial and requires considerable linguistic ore-knowledge as well as sensitivity to context. Roughly speaking, we may identiy as

a metaphor any expression in which a vehicle-word creates tension with a topic-

term or an implied topic. In “she is a rose” the topic term “she” stands in semantic

tension with the underlined metaphor-indicating vehicle-word “rose”. Frequently,

the topic needs to be partly inerred (“dirty-keeled swans” or ships) or wholly

so (“silly ass!” or a dumb person and “attach the mouse to the keyboard” or an

electronic device) (Goatly, 1997, ch. 7). One o the difficulties is that most meta-

phors are not realized as copula constructions (A=B) like “she is a rose”, but in a

great many other syntactic orms (listed in Goatly, 1997). Most metaphors are not

even realized through noun vehicles, but verbs and prepositions (Cameron, 2003).

For this and other reasons,2 inerring an unstated topic is a skill in its own right

that metaphor researchers need to acquire. In “the silence was slashed by a fierce

yell” the implied topic is “[hearing a] sudden acoustic quality”, and contrary to the

superficial appearances, not “the silence”. Note also that the tension responsible

or metaphor can be purely contextual-pragmatic. Te expression “the Rottweiler

behind the bar” may require the hearer’s knowledge o whether an actual dog or a

person is present to ascertain whether the expression is a metaphor and to figureout its topic (c. Steen, 1999). Te same is true o “get to the point”, which in any

standard context is an injunction regarding communication, not physical motion.

Even more radically, recognizing “no man is an island” as being metaphorical re-

quires the inerence that the literal meaning is pragmatically irrelevant in all con-

texts (other than very ar-etched ones).

By a general definition in metaphors the vehicle words have a physical, senso-

rial (non-abstract), more precise, historically older, or otherwise more basic reerence

than their meaning in the given context (Pragglejaz Group, 2007). Hence, (a) oneneeds to establish what the contextual meaning o the expression is, (b) whether a

more “basic” reerence can be ound elsewhere in contemporary usage, especially

in a lexicon. I no such reerence is ound, the contextual expression is literal. I,


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however, a more basic reerence is ound one decides in a last step (c) whether it

contrasts with the contextual reerence, while also involving an element o com-

parison. Tis helps exclude phenomena o semantic/pragmatic tension that arenot strictly metaphorical. o differentiate metaphor rom metonymy we need to

ensure that the contextual and basic meanings are not related by being part o

the same rame or domain. o differentiate metaphor rom polysemy we need to

ensure that the contextual meaning is still somehow understood in relation to the

basic meaning, i.e. that some conceptual transer occurs. Te reader is advised to

consult the Metaphor Identification Procedure (MIP) developed by the Pragglejaz

Group (2007), as well as to read Steen (2002), Heywood et al. (2002) and Cameron

& Maslen (2010b), who discuss these issues and the numerous possible types o

borderline cases in detail.

Another vital decision concerns the unit size o analysis. Should the maximum

text span o a “metaphor” be single words only, multi-word, or even whole sen-

tences? While the MIP advocates screening every single word or metaphoricity,

this atomistic strategy can be unwieldy and overly time-consuming, in addition

to being psycholinguistically implausible. Even simple multi-word metaphors like

“get to the point” need to be split into our separate analytic units o which three

vehicle words (“get”, “to”, “point”) may be recognized as metaphorical. Te same is

true or “we have a mountain to climb”. Conversely, the drawback o using multi-word units is that this strategy is less reducible to a simple rule o thumb and cre-

ates many boundary cases. An ideal solution does not exist. At the theoretical level,

the MIP recognizes that each single word o a sentence unctions as a backdrop or

the others against which their contextual “basic reerence” and thus metaphoricity

are decided. Te approach thus has a certain degree o implicit holism. In any case,

scholars who study large corpora will ofen find the the afforded gains small com-

pared to the added workload that the MIP’s piece-meal procedure necessitates.

Grouping metaphors into coherent sets. Afer the identification stage, one can beginto ask o what type a metaphor is and what it shares with others. As this is explained

later (2.3) I shall skip ahead one step and assume the coder has finished with tag-

ging the metaphorical expressions in the corpus or source and target domain.

Now the task is to find systematicity in the data that reveals something about a

discourse’s key topics and their conceptualization. Tis is done by grouping meta-

phors into sets with a shared conceptual basis. Many, although not all authors

assume that systematicity results rom culturally shared “conceptual metaphors”,

i.e. sets o correspondences between conceptual domains that drive discourseproduction (Lakoff & Johnson, 1980). Goatly (1997, 2007) also calls these “root

metaphors” or “themes”. Te discourse dynamics ramework with its emphasis

on discursive context preers to speak o “systematic metaphor” (Cameron, 2007;


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Michael Kimmel

Cameron, 2010).3 Whatever the nomenclature, metaphors may have a shared basis

when they are (a) about the same or related targets and are (b) coherent in imag-

ery, propositional content, and inerences (which can either originate in the sourcedomain alone or in its interaction with the target). Here is a amous example rom

Lakoff & Johnson (1980, p. 4) or a set o common lexical expressions conorming

to these criteria:

Table 1. A lexical set with a shared conceptual basis

Your claims are indeensible. / He attacked every weak point in my argument. / His criticisms

were right on target. / I demolished his argument. / I’ve never won an argument with him. /

You disagree? OK, shoot! / I you use that strategy, he’ll wipe you out. / He shot down all o my

arguments.

Next, the expressions o the set are grouped together under a metaphor ormula

like . Finding such a summarizing ormula is, or better or or

worse, guided by an intuition o what counts as conceptually similar. Te research-

er aces a tricky decision: How generalizing and broad should the ormula be? One

set o metaphors could point to the ormula

, another to -

, a third set to , and a ourth set to

. Tese our ormulas may either be posited

to be separate or subsumed under the generic ormula . What

is more, although the metaphors are logically all war-related, some aspects like

intensity or entering can potentially be subsumed under orthogonal sets as well

(see 4.2). While grouping expressions together remains a refined interpretive skill,

we shall later see that annotation sofware provides a natural way o “collecting”

similar metaphors via codes and ways o dealing with orthogonal sets.

Analysis o unctions. Optionally, the researcher can analyze the conceptual map-

pings or their discursive unctions (c. Goatly, 1997, ch. 5; Semino, 2008). Tis

means getting an idea why a specific type o metaphor is recurrently used and to

what extent the uses vary in cognitive or discursive unction. Whether this is in texts

aiming at persuasion and explanation or in spoken discourse, potentially interesting

unctions o metaphor may include how speakers (a) highlight and hide aspects o

their topic or rerame it, (b) compress inerences or create complex analogies, (c)

evoke emotions like pride, pathos, or contempt, (d) evoke vivid quasi-perceptual

imagery, (e) create argumentative impact and grab the audience’s attention, () cre-

ate common ground in discourse, (g) mark discourse boundaries, or achieve (h) in-group marking and intimacy, (i) humor and hyperbole, and (j) euphemism. Te re-

quired analytic task remains deeply hermeneutic in that one needs every metaphor’s

context, a good knowledge o the entire discourse and, in some cases, a guiding


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ramework (see 2.9). Yet, sofware can assist even here i so desired. Metaphors can

be additionally tagged with unctional codes to later explore which metaphor type

goes with which unctional types (i.e. through code co-occurrences).Getting a quantitative overview. Although this is seldom realized, basic quantita-

tive inormation is essential to qualitative research. Te sufficient requency o a

pattern is a prerequisite or postulating that a conceptual metaphor or any other

kind o systematicity applies to a discourse (c. Goatly, 2007). Moreover, the schol-

ar may want to select conceptual metaphors by their relative importance or get an

overview o how diverse mappings are in the corpus. She may also aim at compar-

ing o metaphor across sub-corpora (e.g. lef vs. right wing newspapers, male vs.

emale speakers) or across sampling times (weeks, months, years). Such compari-son can involve type and token requencies, metaphor diversity or a given target

or across all targets, metaphor requency per word or per analyzed document/

interview, or metaphors bursts (see below). As Schmitt (2005) suggests, metaphor

sets can also be compared to wider discourse trends either by using corpus tools

that access so-called reerence corpora, by using metaphor databases, or simply

by comparison to previous metaphor studies. A comparative view can showcase

conspicuously absent patterns or weigh a metaphor’s relative import in the total

picture. More generally, qualitative researchers benefit rom a basic grasp o what

corpus linguists do with sofware like WordSmith ools (Deignan, 2005; Deignan

& Semino, 2010). A basic grasp o the logic o reerence corpora and indicators

such as unusually requent words (“keyness”) is helpul as well.

extual cohesion between metaphors. Retrieving metaphor sets that maniest a

shared logic amounts to studying discourse coherence across a corpus. By con-

trast, in a cohesion-based analysis we probe or textual adjacency patterns between

metaphors, or example to identiy metaphor bursts (Cameron & Stelma, 2004;

Corts, 2006), to explore interaction types o cohesive metaphors (Goatly, 1997,

ch.9) or to study cohesion devices that link metaphors in the same sentence or

argument (Kimmel, 2009b, 4.3). With the appropriate sofware one can also com-

bine both perspectives. One may search or cohesive metaphors under the added

constraint that they be coherent, e.g. all -related metaphors in a local cluster.

Or, one may study the dynamics o metaphor in spoken discourse regarding how

one speaker explicitly rejects, accepts, expands, or renegotiates metaphors by the

other (Liebert, 1997; Cameron, 2010). Both kinds o research imply that we look

at coherence (or disparity) together with cohesion (or distance).


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Michael Kimmel

. Why qualitative procedures matter

Some readers may wonder why, with the availability o corpus sofware, a manual

procedure o metaphor identification is still worthwhile. First, a manual procedure

remains the only way to be context sensitive. Some metaphors are not signaled as

such and require one’s knowledge o an extended portion o the surrounding text.

(A concordance window o some 20 characters in corpus sofware can make us

mistake an implicit metaphor or a literal expression.) More importantly, manual

identification remains the only way to be comprehensive. It ensures that we capture

the ull range o lexis whereby a conceptual pattern is maniested. For instance, in

the study o building-related metaphors it is more than unlikely that a corpus lin-

guist would run a machine search or “Heath Robinson structures” (or even knowthe expression); yet this rare expression turned out to be requent in my EU case

study. Unortunately, corpus sofware restricts us to word lists with a ew dozen

lemmas. Tis invariably lets us miss less requent, creative, or overlooked expres-

sions. In manual coding no metaphors are overlooked, whether their lexis is typi-

cal or not. Still more importantly, corpus linguists risk missing whole metaphor

categories, simply because one cannot ully guess in advance what source domains

a corpus includes and will ail to run a search or some. By contrast, manual cod-

ing begins in a strictly bottom-up ashion. Te researcher first grows amiliar with

the corpus and discovers the typical source domains incrementally. Usually, she

will finalize her coding manual with rules, categories and anchor examples only

afer an explorative coding o a fifh o the corpus or so.

Manual metaphor identification is thus time-costly, but it captures the compre-

hensive range o tokens or a given metaphor type and captures all metaphor types

in the corpus, i so desired. Manual work is also worthwhile beyond metaphor

identification proper: It assists theory building by providing the scholar with an-

chor examples, a eel or metaphor subtypes, and a basis or judging whether the

study benefits rom differentiations e.g. between ontological, orientational, andstructural mappings. What is more, manual coding osters an acute awareness

o borderline categories (e.g., “Do very general ontological metaphors qualiy?”

“Where is the cut-off point to polysemy?”) and provides a eel or optimal text

unit size (“Should I tag vehicle words only, or their surrounding phrases, clauses,

or sentences as well?”). In all o these respects it is unwise to start off with precon-

ceived rules, beore having a grasp o the data. Overall, manual coding urnishes a

superior approach or researchers who

(1) aim at comprehensive metaphor coding, i.e. no lemmas and no metaphor

types excluded,

(2) want to get a grip on the (usually great) diversity o metaphor types that

occurs in a real corpus, and


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(3) aim to address complex research questions about metaphor coherence and

cohesion.

On the other hand, it is evident that the time manual coding requires limits itto medium size corpora o, say, 150–1000 newspaper articles, 5–40 interviews or

3–10 literary texts per researcher. Tis brings us to my next topic, the annotation

tools needed or applying a qualitative approach in that data range with reasonable

economy.

. Coding and analysis with .ti

Prospective metaphor researchers are aced with the tasks o text annotation, re-

trieval, filtering, data searches, perhaps some basic number-crunching, and, in-

creasingly, the management o large projects in teams. State-o-the-art sofware

like .ti, ,4 or NVivo is an asset or all o these. In old-style studies

texts were annotated on the margin or cut-and-paste was used to compile quotes

rom text editors. Qualitative tools now streamline the basic annotation process

through simple drag-and-drop rom code lists. Later filters and complex data out-

put options can be applied. Is sofware a matter o mere expediency then? Te

answer is “no”. Sofware promotes an economic workflow, allows browsing vast

ranges o data, as well as sharing, merging or comparing sub-projects in a team.

Judiciously applied sofware also helps meet the criteria o transparency and ex-

plicit procedures (see 1.1 on validation checks and audit trails). Researchers with an

all-in-one grasp o the data and visualization aids structure their hermeneutic task

effectively and minimize error or oversight. Besides these metaphor-unspecific

virtues, sofware enables some o the specific procedures listed in Section 1.2 and

allows us to implement others with unprecedented power. How this is done will

be the main topic o this section. Later I will illustrate how sofware can acilitatequantitative checks, which may in turn help with the qualitative steps.

. Sofware or studying metaphor in discourse

Let us begin with a brie tour d’horizon. One widely available option is to custom-

ize general-purpose sofware or metaphor research. For example, Cameron and

her colleagues rely on Excel unctions (Cameron & Maslen, 2010a) or most tasks.

Excel has powerul sorting options and allows using handy output unctions like

pivot tables (Maslen, 2010). A second option or researchers with programming

skills is to develop specialized sofware such as VisDis, a sofware package devel-

oped and used by Lynne Cameron earlier. Tis caters to special research needs,


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Michael Kimmel

such as metaphor distribution plots, tracking o ontological metaphor types, and

other measures that capture the dynamics o metaphor in discourse (see Cameron

& Stelma, 2004). A third option is to work with tools like , which the MaxPlanck Institute Nijmegen designed or combined video and audio coding (Hellwig

et al., 2010). Tis reeware tool asks researchers to create code systems at various

tiers, each with their separate categories, and allows ormulating complex searches

in the data. Te principal strength o is that a score-line gives the researcher

ull visualization o how discourse transcripts and videos and the annotations at any

tier overlap. It will easily allow coding metaphors in gesture in parallel to spoken

metaphors (Cienki & Müller, 2008; c. Forceville & Urios-Aparisi, 2009 or urther

perspectives on multimodal metaphor). However, working with complex category

systems is not among its major strengths because the codes are not so easy to

re-hierarchize or shuffle; also multiple coding o the same text unit is limited.

My preerred option is to work with commercially available sofware packages

or qualitative research like , .ti, NVivo, Hyper, Qualrus,

and ransana. Te best o these tools are “methodologically neutral”, meaning that

they can implement any kind o approach to qualitative research (see Lewins &

Silver (2007) or a comparative evaluation). Teir specific strength lies in the ease

o coding, a plethora o powerul output and filter options, as well as reely com-

binable search procedures or tracing cross-connections in the data. I have optedor .ti 6 or various reasons (c. Friese, 2012). One concerns its excellent

visualization acilities or creating networks o codes, memos, text units, and other

items, which encourages theory building through “mindmaps”. Another benefit

is “hyperlinking” o spatially disjointed, but logically connected text units. Next,

.ti supports multimedia coding, integrated searches across data ormats,

and the synchronization o transcripts with video- or sound-files. Finally, the tool

does not enorce fixed ontological code hierarchies, promotes inductive work, and

maintains the scholar’s flexibility in customizing a project. (We may note in pass-

ing that this allows strategies o metaphor analysis other than what I shall de-

scribe.) o be air, we should also admit some limitations o .ti 6. Compared

to VisDis creating distribution plots and graphs that compare metaphor types is

not hal as easy in .ti (but manageable with SPSS exports). Compared to

multi-media coding is currently not optimally supported in .ti 6, be-

cause no score-line is available or video data or sound-files, whereas the recent

upgrade to version 7 introduces this kind o acility.

. Setting up a project in .ti

I shall now outline a ull-cycle o metaphor coding and analysis with .ti. Te

necessary “how-to” will be presented in steps, while saving a ull methodological


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justification or Section 4. First, researchers who have assembled their corpus eed

it into .ti in an electronic ormat. Tis can be an image, a text-file or PDF,

a video, a sound-file, or a synchronized transcript (or listening to the sound-filewhile reading the text). I will stick with simple texts here, such as interview tran-

scripts or newspaper articles. ypically, the researcher will designate each text a

sub-unit o the project (given that data patterns can later be searched across sub-

units). Once the data have been ed into .ti, the text to be currently worked

with is chosen and appears in the lef panel on screen. Te code system can be dis-

played as a list in the right panel (see Figure 1). Te area between them is reserved

as a text margin or the annotations.

. Compositional coding

Te most time-consuming and decisive stage in any project is the coding/an-

notation. What is the purpose o codes? Codes are tags attached to several text

units, which later unction as data containers to retrieve theoretically equivalent

expressions. A useul metaphor or codes is to see them as “shopping-carts” or

text units that are used while running through a text, with the aim o collecting

similar metaphors. In the later analysis, the contents o each cart can be retrieved

and displayed either as a list or as a shortlist with the option to click back to thequote in its context. o be able to do this, codes have to be assigned to the text in

one or several thorough work sessions. Usually the researcher begins by reading

the whole text once. Ten, she goes through it more slowly to identiy metaphors.

Te metaphors are marked and coded, two steps I will look at in greater detail now.

ext units identified as metaphors are marked by highlighting with the mouse

cursor (lef panel in Figure 1). Ten two strategies are possible. When a step-by-

step procedure is chosen a bracket is assigned to the annotation space in the central

panel o the screen. It marks the text segment or later coding. When we choose

the (deault) two-in-one procedure we immediately assign a code to the selected

text unit through drag-and-drop. Te code we consider fitting is selected rom

Figure 1. Marking a metaphor unit


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Michael Kimmel

the list on right and dragged onto the highlighted text. I we wish to code various

kinds o tropes, we may also choose to first assign a generic code like to

differentiate other general phenomena like , , or and moveon to more specific codes only later. Te quotation size is up to the researcher. It

can be changed afer the first coding i necessary. Te metaphor units in my case

study below were, or practical reasons, a clause or at most a sentence in length,

but seldom single words. Whenever a sentence involved several independent met-

aphors, these were tagged separately.

Te next step is to assign a more precise ontological identity to every marked

metaphor unit. I recommend a specific coding logic here, rooted in a theoretical

consensus about what metaphor is. o describe a metaphor appropriately one must

identiy (i) its wider source domain (ii) its wider target domain and (iii) the specific

amount o inormation that actually gets mapped between them (Lakoff & Johnson,

1980; Goatly, 1997). For the purposes o discourse analysis, metaphors belong to

the same type only when they share source and target, and by a yet narrower con-

straint, only when the same inerences or images get mapped between them. Te

latter aspect is probably best dealt with more inormally (see 2.8). However, the

system implemented in .ti should allow us to systematically assign source

and target codes to each metaphor unit. For instance, the expression “launched the

European project” receives the codes “source: paths” and “target: EU integration”which will later be subsumed under the conceptual metaphor ormula EU -

. Te screenshot in Figure 2 shows how the researcher chooses

source domain codes rom the (in this example already ully “populated”) code list

on the right and drags them onto the marked quotation.

Usually the text is read meticulously and tagged piecemeal, ofen in more than

one sitting. Occasionally, however, a strategy o semi-automated coding can be

employed, as .ti also supports searches or predefined word-lists. I we are

certain the vehicle words in our list exhaustively circumscribe the aimed at phe-

nomenon hundreds o metaphors can be automatically marked-up with codes in a

matter o seconds. However, this simple corpus tool-like acility makes sense only

or the ew metaphor types that can plausibly be restricted to a well-circumscribed

range o lemmas (see 1.3).

Figure 2. Assigning a source domain code (circled)


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How does the code list itsel come into existence? As to the source domain

codes, many researchers may want to predefine them by compiling common met-

aphors rom the literature or prior knowledge o the field (= deductive approach).Alternatively, codes can be created on the fly while exploring the text (= induc-

tive approach). It makes sense to combine both strategies by starting with a list o

requent sources, but allowing or augmentations. Even i experienced researchers

will have “usual suspects” in mind it is unwise to limit the range o source codes

based on intuition, because subjective bias will enter.

Te target domain code list depends on the kind o project one has in mind.

Tere are two possible scenarios here: I one aims to code all metaphors indepen-

dently o what they are about, i.e. i all target domains remain eligible, the list has

to remain open and will grow through inductive work until some saturation point

is reached. I, by contrast, the project has a deliberate thematic ocus on particular

domains — say, metaphors or religion, economics, law, or politics — the researcher

will delimit in advance which targets are eligible. argets absent rom the research-

er’s predefined list will thus be lef uncoded. Such off-topic metaphors may be high-

ly requent both in written and spoken discourse. Even in a corpus with a narrow

thematic ocus off-topic metaphors may go up to a margin o 30% or so, among

other things because a certain percentage o metaphors always serves discourse-

organizing rather than content-related unctions, but also because o asides, etc.

. wo-tier coding o source domains

Regarding source domain coding, a particular complexity highlighted by CM

is commonly overlooked in practice. Many kinds o metaphors have been ound

to involve image schemas as primary scaffolds or conceptual structure (Lakoff &

Johnson 1999). Metaphors with similar underlying image schemas (e.g. ) can

share meanings even when their rich domains (e.g. “boat travel”) differ, and vice

versa. More generally, each metaphor can be described rom two viewpoints, with

two cognitive “layers” that inorm metaphor processing. Te expression “the state

ship conronted an iceberg” invites both and image schemas that

are shared with non-nautical metaphors such as “running into a wall o silence”.

Parallel to that, our example calls up knowledge about ship navigation, crews, and

captains shared with any ship metaphor, but quite independently o collisions or

paths. One layer is the image-schematic core representation that “carries” the ontol-

ogy o a mapping (c. “Invariance Principle”, urner 1991), while the other layer,

the cultural exemplar , piggybacks on it by adding richer knowledge and inerentialentailments. As a matter o principle, metaphors should be coded at both levels,

as different similarities with other metaphors are brought out by each layer. A bias

in avor o one way o grouping metaphors limits the quality o any study (see


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Michael Kimmel

Section 4.2). Tereore both potential layers o actual metaphor processing — the

province o psycholinguists and thus beyond our ocus here — reflect possibilities

or grouping metaphors in discourse research. o this end, I recommend using a listo image schema codes and a separate list o semantically richer codes (in addition

to a target domain list). Both lists should be used in annotating a given metaphor

i applicable. I call this principle two-tier coding. o which metaphors does this

not apply? First, the ontology o some mappings does without image schemas and

purely rest on what I called a rich domain (Ruiz de Mendoza 1998). Conversely,

many metaphors have little structure besides image-schematic one. Tis is typical

or Lakoff and Johnson’s (1980) category o ontological metaphors like “have a lot

o know-how” () or “be in love” () and or orientational meta-

phors like “rising spirits” (-). While entities, containers, and verticality

may be urther elaborated in principle, they are all there is to these examples.

Let us turn to the practical aspects o two tier-coding. Sometimes, using mul-

tiple items even rom a single code list improves later data output. For example,

speaking o the EU constitution as entering “through the back door” the image-

schematic source codes - and together best capture the implied

motion (in addition to the rich source domain code ).

As Figure 3 illustrates, multiple target codes can be equally helpul when two

or more targets rom the list meet in a single phrase. In the phrase “EU plan clearsSpanish hurdle” the target “Te EU reerendum” is mentioned, while the target

“EU-integration” is implied, because a successul national reerendum contributes

to EU integration. In addition the vehicle word “hurdle” was coded with -

and image schemas and the rich domain , totaling five specific codes

here (not counting the generic code ). Note that each additional code

simply creates an additional grouping option which can (but need not) be picked

up in the later analysis.

Figure 3. Various source and target codes attached to one quote


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. Reconstructing conceptual metaphors and retrieving quotes

For what I shall call compositional coding, the tagging o all metaphor units needs

to have been completed. Now theoretically inormed work can ensue to cross-link

the data. For reconstructing systematic metaphors source or target codes in and o

themselves are not terribly inormative. For instance, retrieving all quotes that go

with the source domain “path” is not a prototypical research aim, as the output will

be too unconstrained.5 A more typical aim is to compile all expressions exempliy-

ing a ull mapping like EU . Yet, the coding logic adopted

precludes that mappings are directly retrieved (i.e. we have no ull metaphor or-

mulas in our code list we can click on). Rather, mappings arise in a combinatory

ashion via code co-occurrences. We thereore need to explore which sources gowith which targets in .ti. Suppose we choose a target such as “EU integra-

tion” and list all sources applying to it under a rubric (e.g, paths, orces, buildings).

A unique combination o codes like [target= EU + source= ] will then cor-

respond to the mapping relation EU . I shall show in

Section 3.4 that tabular overviews or each target domain are a handy entry point

or checking all relevant code co-occurrences.

able 2 below explores the target “EU integration” rom the newspaper Sun.

Each cell stands or a code co-occurrence and defines a set o expressions with a

similar logic, in other words: a systematic/conceptual metaphor. Under the header

o the aorementioned target domain the table lists the various systematic/concep-

tual metaphors associated with the target, thus providing an overview and reveal-

ing their relative weights. Since the number o hits or each co-occurrence is also

listed we can easily pre-select the most requent patterns or impose a relevance

threshold to exclude “data garbage”. In the present example I discarded the low

requency co-occurrences below three hits. Te remaining patterns can now be

individually subsumed under (broad) metaphor ormulas like EU-

/ / / , etc.

Table 2. Co-occurrences or a single target: Te source domains are sorted by requency

(in brackets) to help us find the more important patterns.

EU-integration is ...

Image-schematic sources Rich sources

Movements, paths and object transport

Forces

Center-periphery

Up-down

ogether-apart

[65]

[11]

[6]

[5]

[3]

Vehicles, drivers, and journeys

Buildings

War and aggression

Social relations and groups

Sports, games and play

[8]

[8]

[3]

[3]

[3]


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Michael Kimmel

Once we have inerred the fitting metaphor ormulas, our aim is to compile

the expressions or each in order to start the interpretive analysis or a write-up.

For this we need the .ti “query tool”, a data output machine that allows usto retrieve sets o theoretically equivalent text units. ext retrieval happens in a

hypothesis-driven ashion. Te printed co-occurrence table tells us what to look

or in the query tool. We simply enter source and target codes, connect them by

Boolean AND (see below), and let the machine produce all expressions belonging

to one set. Figure 4 shows both the query tool and an output report.

Each query procedure results in a list o theoretically equivalent metaphors.

Tis Boolean co-occurrence search needs to be repeated or every source-target

pairing we want to investigate (see 3.4).6 Although this is the mainstay o the

method, an almost unlimited range o complex hypothesis testing can be done on

top o this. Basically, the .ti query tool offers three separate, but combinable

search modes:

(1) We can search or text units belonging to single codes, code combinations (A

and B), or complex patterns o Boolean logic (e.g. “A or B, but not C”; “A and

B or C and D”, “all but A and B”, “A or B or C”). We can also pick out text units

belonging to a code amily created or that purpose (e.g. a combination o the

different kinds o orce and path related image schemas).

Figure 4. Query tool and quotation output or the metaphor set [EU + “vehicles/drivers/

journeys”]


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(2) We can search or spatial distribution patterns o metaphors in a text or multi-

media file. Te query tool can locate all units that overlap or “rub-shoulders”,

i.e., occur within a chosen text distance. For instance, we might ask i meta-phor units typically are textually adjacent or have non-metaphor words be-

tween them. We might explore i metaphor clusters are distributed equally

across a longer text or occur in special slots only, such as the introduction. We

might explore how metaphor units are embedded in units coded by qualita-

tive context analysis and explore the relative scope o two qualitative research

methods (see 4.5). Or we might explore how ontological metaphors are slotted

in larger discourse metaphors.

(3) We can search or expressions or all codes that occupy a “parent”, “child” or

“sibling” position in a hierarchy o codes once we have predefined this hier-

archy or this purpose. Tis is done in a visual tool which is akin to drawing

a mind-map on a piece o paper. It allows positioning codes in a virtual space

and defining relational ontologies between them, e.g. “supports”, “is a”, “exem-

plifies”, or “contradicts”. Any such hierarchy is reflected in the code window on

our screen.

We can easily combine all three search options and ask “which meta-

phors ollow immediately upon a metaphor cluster o three metaphors reerring to

the target domain power , but none o which has the source domain ” Tis

might sound ar-etched, but similar queries do occur. Hence, the query tool encour-

ages exploring the data in whatever ways fit our research question and can even help

us discover new ones. All in all, .ti provides a solid and variegated basis or do-

ing interpretive work, which will be urther explored in the Sections 4.3 through 4.5.

. Excursus: Te benefits o compositional coding

Tis flexibility is enhanced by combining the tool itsel with the compositional cod-ing strategy. o sum up, compositional coding has three main stages: (1) Te coder

browses the text or the first time and marks text units with a (still undefined) brack-

et or with a generic code like “MEAPHOR”. (2) In a second sitting text units are

tagged with target and source domains codes. (3) Identiying the precise mapping

is deerred to the final analysis; as will be explained later, this is done through creat-

ing a panoramic view o the data, counting the number o hits, and then running

a co-occurrence search or each pattern to access the expressions or the write-up.

Critics will ask this: Why don’t we take the more direct route o applying ullmetaphor ormulas like EU to the expressions? Foremost,

our indirect coding strategy keeps a complex study manageable. By the law o

combinatorics a ew dozen sources and targets are capable o covering hundreds


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Michael Kimmel

o possible source-target combinations. By contrast, the perhaps hundreds o

systematic/conceptual metaphors ound in many corpora render the “one meta-

phor type = one code” strategy problematic. It would make the code list explode.Second, the workflow benefits rom the compositional strategy. Te researcher’s

mind is reed rom the difficult task o phrasing the ormula or the precise map-

ping at the right abstraction level. Te compositional strategy largely circumvents

the demand or re-phrasing the code which new metaphors might otherwise sug-

gest requently during a project’s early stages (see 4.2 or a ull argument). Tird,

making the sources and targets separately identifiable contributes to transparency

and flexibility. Suppose, or instance, one has opted or a code system that im-

mediately created a tag like EU . I the desire should later

arise to find all kinds o paths, independently o “EU integration” the code system

cannot support this kind o retrieval. By contrast, with separate source and target

coding scholars are unrestricted. Tis rees them to selectively search the data by

“what topic the metaphor is about” (= target term) or by “what kind o metaphor

it is” (= source term). I will return to this issue later.

. Te next steps

Source-target co-occurrence tables constitute no more than an entry point. Next,as a matter o good scientific practice, a data check should ollow by using the

.ti “query tool” to run through all quotes o a metaphor set, in order to bring

finer distinctions to the ore and to check to see i the expressions orm a valid

set. Only when they evince a shared logic a metaphor ormula is assigned to the

set. Minor inconsistencies can be dealt with by regrouping expressions to another

set, assigning an independent ormula, or consigning them to the pool o “sub-

threshold” mappings. Sometimes, it may turn out to be helpul to merge two sets,

especially when their tokens are ew and their implications similar (or an ex-

tended discussion o working with the data see Cameron et al., 2010, pp. 119ff.).

. A stratified analysis o sub-sets

Many researchers will wish to urther differentiate the metaphor sets. Te researcher

has now compiled anywhere between 4 and 80 expressions or the same systematic/

conceptual metaphor and aims to group them according to finer criteria. Tis may

happen by manually pasting the expressions rom an output list under separate

sub-set headers or by splitting a code in .ti and creating separate outputs.However, the technical side o it is o little value without the researcher’s theoretical

knowledge. For a proper discourse-analytic approach to metaphor a “stratified” un-

derstanding o its conceptual logic is needed, along several possible lines. We may


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note right away that creating special codes or the ollowing tasks does not work

too well, unless the corpus is very limited. We had best leave this to the researcher’s

interpretive skills afer having created the code output in order to keep the codesystem manageable and attention ocused on one level o analysis at a time.

As probably the most important criterion or sub-divisions we may now pick

up on a claim made earlier. It was said that identiying the precise mapping relation

is a key aspect besides the source and target descriptions. Researchers should thus

be keenly aware o the mapped attributes that underlie a metaphoric expression,

i.e. what Goatly (1997) calls the grounds o a metaphor. Grounds pertain to the

analogy or similarity a mapping is based on. In “the past is a oreign country; they

do everything differently there” and in “a aint trickle o smoke” the italicized part

o the sentence expresses the basis or the mapping or at least provides a clue. Te

grounds may or may not be linguistically expressed. Many metaphors are conven-

tional and need no spelling out o the grounds, or they are novel and creative (such

as these two) and leave it to the reader to attribute the ground to the expression.

Importantly, mappings rom a single set in our output may go with quite vary-

ing grounds. Which ground is intended can usually be inerred rom the context,

although sometimes several readings remain open. For example, the metaphor

“Te EU’s common house”, depending on the context, might have been created

because o the inerence that the EU is well-designed and solid, that it protects itsowners, that the latter share responsibilities and belong together, or that one enters

it only by being allowed to do so. Since we usually want to study metaphors with

respect to the inerences they create, the grounds constitute a highly relevant crite-

rion or sub-grouping metaphors rom a set. Tus, rom the inerential viewpoint,

the ormula “EU=house” proves to be overly abstract and requires sub-divisions

in the applied analysis.7

A still more complex way to achieve stratification is to check whether a meta-

phor is “story-like” and i speakers “narrate through metaphor” (Johnson, 1993,

ch. 7; Eubanks, 2000). When this is the case we can search or similar narrativiza-

tion patterns o a basic mapping. In this respect, a key distinction runs between

central/core mappings and metaphor scenarios spinning-off rom these (Musolff,

2004; c. Semino, 2008, pp. 219–222; Kimmel, 2009a, pp. 89–92). A core mapping

is a generic structure such as EU . It may be linguistically explicit

in expressions like “the EU amily”, but ofen remains an inerred background

structure underlying several expressions. It ofen consists o a simple role ascrip-

tion, but need not speciy a concrete action and is thus not story-like. A metaphor

scenario, by contrast, is a more contingent, dynamic characterization building ona core mapping, such as is triggered by the expression “the EU parents are getting

angry with their most recent offspring”. Te scenario usually specifies what hap-

pens between several roles in a small metaphorical story and is essential to the


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Michael Kimmel

argumentative value o the expression. Tese specific mappings are relational and

put several metaphorical entities in their respective slots. Te logic o metaphor

scenarios creates added ways o sorting the data into sub-sets. A core mappinglike EU entertains a one-to-many relation with the metaphor

scenarios , ,

, etc. Likewise, EU spawns

various creative scenarios like EU

or . Identiying such sub-

sets makes us see the different implied inerences (and grounds), while keeping the

common higher level in evidence.

Due to different grounds, it is quite possible that a set o metaphors rom a

single core mapping does not share the same image-schematic basis. Te solid

build o the EU house is different rom its entry condition, or instance. Tis brings

us to a yet finer stratification option. We may differentiate diverging role ascrip-

tions and interaction patterns o otherwise similar image-schematic scenarios.

ake, or instance, metaphors that concern the relationship o Britain to the EU.

In one sub-set the EU “enguls” the UK and in another the metaphors talk about

its “entering” the EU. Evidently we may group them together, because both real-

ize the image schemas with the same two agents. Yet, the UK’s role

shifs rom a passive nation surrounded by an expanding container to an activenation deciding to join a static container. So the image-schematic logic is slightly

different. For one thing, “entering” lacks the emotional connotation o ear that is

present in being “enguled”, and or another, the implied agency totally differs. For

this reason, the researcher may not want to base the analysis on image schemas

at the canonical abstraction level, when a discourse’s intrinsic logic systematically

distinguishes sub-variants like and .

. Discourse unctions

Finally, researchers may take interest in the cognitive, rhetorical, and discursive

functions o each metaphor set (see 1.2 or a list). Some key questions are: What

effect does this metaphor have that its literal counterpart (i any exists) does not?

How does the metaphor rame its topic in comparison to possible other meta-

phors? And what role does it take on vis-à-vis a text’s overall purpose? When

we look at individual metaphoric expressions in co-text and context we realize

that metaphors under the same general ormula may not always ulfill the same

rhetoric unctions (due to different grounds but also due to negation, etc.). Tus,even when we have generated valid metaphor sets these need not be unctionally

monolithic. And conversely, when we “think through the data diagonally” we may

find similar unctions in different metaphor sets.


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Michael Kimmel

crisis, agents, politics in general which circumscribe the metaphor-based discourse

around the constitution. Metaphors reerring to these target codes were easy to

select in advance. Metaphors reerring to EU-unrelated target concepts, e.g. “itis striking that” were excluded (an estimated 30% o all metaphors). In addition,

27 image schemas and 54 rich source domains were implemented as codes (see

Annex I). Tese codes were developed inductively to some extent.

. Quantitative basic data

As a basis I explored the data in quantitative respects not discussed so ar. A first

good option is to extract and compile basic quantitative data in .ti:

Table 3. Quantitative summary o corpus

Coded

articles

Word

count

Words per

article

Coded

metaphors

Metaphor codes

per article

Metaphor codes

per word

Guardian 501 321411 642 1588 3,2 0,005

Sun 174 41704 240 986 5,7 0,024

Tis table shows some interesting differences between the two newspapers con-

cerning metaphor density (but not yet diversity, see 3.4) against the backdrop o

the length difference between the quality and the yellow-press paper. Tis striking

disparity may be discussed i desired, but remains beyond my present scope.

. Quantitative survey o targets

A urther option I recommend is to generate an only-by-target survey o all meta-

phors. Tis yields a somewhat more differentiated bird’s-eye-view o the project.

Although it says nothing about specific mappings yet, it allows us to compare re-

quency trends in both newspapers in a heuristic ashion. o compute the table, thesum o all co-occurring source codes or a given target code are added up in a line.

Tis is done or all targets. Let us look at the set o targets rom my study concerned

with the EU itsel in as depicted able 4. It becomes clear that the EU (understood

as an entity) is by ar more requent than any o its more specific sub-topics, al-

though EU integration is also an important topic. Evidently, comparing the topics

that receive metaphoric recognition in each newspaper independently o the ac-

tual mappings may become a key heuristic beore starting the interpretive analysis.

Although it conflates metaphors o different sorts, this data output option offersorientation about a corpus.


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. Reconstructing ull mappings rom co-occurrence tables

Next we can identiy ull mappings. As was explained earlier, we need to check

which source codes go with which target codes (and how ofen). We identiy all

“unique combinations” by running a co-occurrence search in the .ti query

tool. able 5 is again based on the above seven targets related to the EU (see column

on the extreme lef). Te columns “Guardian” and “Sun” list all specific sources that

go with the target on the right, ollowing the logic o metaphor diversification in

Goatly’s terms (1997). All patterns below the threshold o our hits were deselected,

to ensure that only discursively potent mappings enter the qualitative analysis.8

Table 5. Co-occurrences o sources and targets (grouped by sub-targets related to the EU)

EU targets Co-occurring sources Guardian Co-occurring sources Sun

EU is… Body [22] Body [13]

Buildings [29] Buildings [16]

Center-periphery [34] Center-periphery [8]

Containment, engulment, breach [12] Containment, engulment, breach [9]Machinery and technology [24] Machinery and technology [11]

Movements, paths, object transport [28] Movements, paths, object transport[7]

Personification [11] Personification [6]

Structure-lack o structure [20] Structure-lack o structure [6]

Superstate [37] Superstate [45]

Up-down [8] Up-down [6]

Vehicles, drivers, and journeys [29] Vehicles, drivers, and journeys [8]

Animal [4] –

Animate being / agent [7] –

Business [5] –

Creation / monster [7] –

Crime and conspiracy [4] –

Table 4. argets and their requencies

Target domain Co-occurrences of the target with all types of sources

GUARDIAN SUNEU is… 850 378

EU-unctions/unctioning are… 48 2

EU-idea is… 112 48

EU-institutions are… 99 30

EU-integration is… 276 96

EU economy is… 41 31

EU enlargement is… 58 6


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Michael Kimmel

Table 5. (continued )

EU targets Co-occurring sources Guardian Co-occurring sources Sun

Forces [22] –

Health and disease [5] –

Plants and growth [13] –

Religion, ritual and sacrifice [6] –

Social relations and groups [21] –

Sports, games and play [12] –

ogether-apart [15] –

Streamline [14] –

Part-whole [7] –

Natural orces [4] –

War and aggression [6] –

– Intact objects and destruction [8]

EU functions

are…

Forces [14] –

EU idea / project

is…

Lie and death [4] –

Intact objects and destruction [6] –

Structure-lack o structure [5] –

– Dreams and sleep [4]

EU-institutionsare…

Movements, paths, object transport [5] –

Body [5] –

Buildings [5] –

Machinery and technology [4] –

ogether-apart [4] –

– Near-ar [6]

EU integration

is…

Movements, paths, object transport [61] Movements, paths, object transport[24]

Buildings [8] –Center-periphery [6] –

Containment, engulment, breach [5] –

Forces [11] –

Superstate [6] –

Up-down [5] –

Vehicles, drivers, and journeys [8] –

– Near-ar [6]

EU economy is… Forces [5] Forces [4]

EU enlargementis…

Movements, paths, object transport [7] –


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Tis tabular overview is a multiple asset. Almost at a glance the trained eye can

glean important metaphor ormulas like the EU , EU , or

EU and discern the patterns that quantitativelydominate. Te table thereby suggests a preerence order or the analysis. o de-

velop methodological reflexivity, we can also pinpoint codes that seem too broad,

e.g. the requency o orce metaphors may inspire a check to see i the category is

too global. A data clean-up is equally supported. For instance, the low number o

hits or the targets “EU unctions” and “EU economy” suggest that they might be

subsumed elsewhere. Finally, the list allows a quick comparison between sub-cor-

pora in terms o their metaphor diversity. We may ask why the Sun has strikingly

ewer metaphor types than the Guardian. It can be especially instructive to subject

to closer scrutiny source domains that appear exclusively in one newspaper and

inquire into the journalistic purposes or not using metaphors used by other writ-

ers. Note, however, that a meaningul comparison o metaphor diversity and o

differences in metaphor requency between sub-corpora necessitates taking into

account sample sizes (see able 3).9

A similar kind o tabular grouping by source domains may be created as a

countercheck. Tis reveals the range/semantic variety o targets onto which a par-

ticular source gets mapped.argets that exclusively occur under the same source

rubric may be combined in the write-up. In my case study, the targets EU and EU were requently coded with “path”, a clue that they were inherently

relatively close and might be collapsed. Detecting such overlaps thereore helps

avoid excessive analyticity.

. Commented write-up excerpt

o illustrate how the table assists a systematic write-up, I have taken rom my

original study a passage that structures the text both by sources (such as “path” and

“journey”) and all seven EU related targets. Basically each relevant cell o able 5

inorms a part o the text, around which I built an argument structure. In addition,

rhetoric unctions are differentiated. I added comments in bold-aced brackets to

explicate the proposed methodology and to connect the discussion to able 5.

Te largely overlapping source domains “paths” and “journeys” create the sin-

gle largest metaphor group in both papers, being slightly more dominant in the

Guardian. [general role of section in the corpus] Because o urther meaning

overlaps in the target, I decided to draw the twin-targets EU and the EU integra-

tion together (the numbers in parentheses will reer to both). Te dominant map-ping ound here is EU . [summary formula] A number

o different evaluations are couched in this metaphor concerning the process, its

aims, and its status, especially afer the French and Dutch No votes [discourse


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Michael Kimmel

context stated]. We find two closely related mappings here: EU

and EU . [sub-vari-

ants] Te latter seems to presuppose an implicit moving entity, most likely the EU.

Regardless o a speaker’s convictions, journeys are useul.

In the Sun (N=7+24), [from cells EU is… + EU integration is…] the pro-EU

camp speaks o “a brave new course or Europe”, “Europe moving orward”, a “per-

ectly sensible way orward”, and that the constitution “does not go ar enough”

(Jean Luc Dehaene) and “shouldn’t stop there” (Rocco Buttiglione). Te critics say

that “the EU has gone so ar down the dangerous route”, warn about “travel[ing]

one inch urther down the slippery slope o European integration”. Tey criticize

that “Brussels is carrying on regardless” or scold “the clowns who have driven

“Te Project” to the brink o disaster”. [scenario variants depending upon speak-er viewpoint] ony Blair is reported to call “or a huge change in direction”, while

oreign minister Jack Straw limits Britain’s involvement by saying “Tis ar and no

urther”. A commentator mentions that Blair “has a unique opportunity to drive

the EU in the direction that is best or Britain”.

A look at the Guardian (N=28+61) [from cells EU is… + EU integration is…]

demonstrates systematically that path attributes are used to reason with: Te

EU travels in a particular direction, at a particular speed, and driven by some

orce [overview of mapped aspects]. Te EU is thought to be “stopping”, “go-

ing on”, moving at a certain “pace”, or being “propelled orward too quickly andambitiously”. It must “be kept going” or “need not proceed orever”. While or

its supporters the EU is “in permanent onward flux” and “moving on”, its critics

aim at “rendering it immobile”, and some skeptics expect that it will “encounter

large road blocks” [scenario variants depending upon speaker viewpoint]. As

to its pace, integration happens in “leaps and bounds” or “two steps backwards

ollowed by almighty leaps orward”. Te EU can be static or dynamic; e.g. it can

be “overtaken by the world economy”. Hence, the EU can metaphorically vie with

other kinds o orceul movement. Or, Europe can move at two speeds concerning

“rumours that Paris and Berlin planned to orm a political union leading to a two-track Europe within the EU, leaving behind recalcitrant states such as Britain.”

As to direction, the EU is “heading in a wrong direction”, the direction may be

“unclear”, “can be cooperative” or “should be changed”. O course, “the end point”

may differ depending on whom one asks. Te EU may be on “the road to ruin” i it

lacks vision. Te required orce or traveling is equally elaborated on. Te ound-

ing athers and important nations are its “driving orces”. Politicians try to “take

it” or “lead it” on a journey or which a “pace is set”, “milestones” exist, and “ellow

travellers” are sought. Te EU is being orced along in “Gordon Brown […] is

brimming with ideas and determined to drag the EU kicking and screaming into

the 21st Century”. Causality does not only issue rom the politicians, but also rom

the constitution’s own driving orce. Tus, in the view o supporters, rejecting the

constitution will “bring the EU to a grinding halt” or “set the European project

back by 15 years”. Te constitution is the causal determinant o the direction o the


29/48


EU, such as when it “turns Europe away rom the path o solidarity and into that

o neo-liberalism”. Te path scenario provides a flexible common ground or dis-

course [reflection on discourse function]. It easily accommodates the crisis afer

the reerendum Noes in France and the Netherlands, when the uture turns out to

be “a rollercoaster ride”, creates “road blocks” and “blocks the path o new nations

queuing to join”. Now, “Europe is new territory”, at a “crossroads”, or it “can neither

go orward nor stay the same”, it is “shuddering to a halt” with integration and

enlargement “stopped in their tracks”. At best, the “way ahead is ar rom clear”

and “the momentum [or deepening, or widening] is broken”. It can “stall” in “the

present impasse” or “stumble along”. Others ask rom every member state “to put

a shoulder to the wheel” in order to move on.

Less requent target domains inherit the path logic. [relation between targetsspecified] In the Guardian, the target European enlargement (N=7) has a “pace”,

it may be a “long and winding road” or pose no “hurdles”, and, depending on

the viewpoint, is a process that “had not been derailed” or “stopped in its tracks”.

Paths or the target EU institutions (N=5) result in “the endless, grinding pace o

institutional change”, the “European institutions inveigling their way into every

nook and cranny o lie”, and “the rotating presidency will plough on”. Te Sun’s

path metaphors are too ew to be considered in these categories [comment on

below threshold data].

In both newspapers, rich images o “vehicles, drivers and journeys” speciy thepaths or the targets EU and EU integration. [reflection on the relation between

image schemas and rich sources] In the Guardian (N=29+8), the EU is “a bicycle:

you keep pedaling or you all off ”, “a ship without a clearly defined course”, a “train

stopped in its tracks” or it can be “streamlined”. In the crisis “Britain can seize the

steering wheel” or the “helm o the EU”. Vehicles are used to speciy the speed and

saety in “bicycling” vs. “an amble”. In the Sun vehicles are ound or the EU target

(N= 8). Blair “takes the helm” or “sets a brave new course” or has “the unique

opportunity to drive the EU in the direction that’s best or Britain”, when Britain

assumes the EU presidency. On the whole, the vehicle code overlaps with the pathcode almost totally and is near-redundant.

At this point a ull analysis would turn to the next source domain rom able 5,

e.g. “machinery”. Space limitations preclude this here, although I shall present an

abbreviated analysis o “machinery” towards the end o the next synoptic section.

. Synopsis o metaphor ormulas and discursive unctions

I shall now urther condense the data or better global insight. My synopsis willdistill the argumentative thrust rom the metaphor ormulas, highlight the actual-

ly mapped aspects/grounds, and define them relative to their discourse unctions.

It is here that we apply stratification options rom Section 2.8. Te above excerpt


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Michael Kimmel

was developed along the lines o the image schema code “paths”, which more or

less coincided with the rich domain code o “vehicles, drivers, and journeys”. As

I shall show now, this data segment is easily given structure by splitting a singlecore mapping into its mapped sub-aspects and scenario variants. I shall also list

a second group below, which I did not have the space to present in more detail

above. It concerns “machinery” metaphors, a rich domain o many overlaps with

the rich domain “buildings” and the image-schematic domain “structure”. Tis ex-

ample points to an important urther purpose o the synopsis, which is to make us

take explicit notice o image-schematic variants within a rich domain and discuss

them, or instance the relationship between the EU’s “motor”, a metaphor

and the idea to “dismantle” the EU, a metaphor. Finally, note the inter-

esting abstract overlap between the journey and machinery sets in the metaphor

“gridlock”, where process and unction meet in a single notion.

EU [generally highlights processes and causes]

– EU → emphasizes prog-

ress as being continuous, erratic, rushed, too inactive, or too slow.

– EU / - → em-

phasizes decisions about the EU’s uture being (un)clear or wrong, and that

the integration process has powerul advocates.

– → emphasizes

the No vote as obstacle to urther integration; converges with “gridlock” o

institutional unctioning and “streamlining” to avoid it (Guardian only).

– → brought up in warnings against

rushing decisions afer the No vote.

– / ’

→ emphasizes the causal role o the constitution or particular politi-

cians or integration.

EU [generally highlights unctionality and its prerequisites]

– EU → Te European institutions and, by implication the

constitution, drive integration (either “smooth running” or the No votes “put

a spanner into the works”, create “gridlock”, “dismantle it”.)

– EU’ / → used to indicate

that some nations are more powerul or more willing to shape European politics.

– EU → emphasizes that the

EU consists o countries with complementary unctional roles.

– EU → emphasizesthat the EU is a complex idea, has a sound structure/needs to be redesigned,

and that the constitution shapes it (as its “blueprint”).

[Further source domains rom the table are summarized in a similar way]


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At the end o a project we have thus arrived at an “executive summary” that boils

down the data, presents sets o core mappings, and applies the discussed stratifi-

cation rules to differentiate them internally. With a summary o a whole corpusat hand, it is possible to discuss the ocus, internal variety, and boundaries o a

discourse, to compare newspapers globally, and to explore discourse coherence

rom a bird’s eye view.

. Analytical potential for discourse research on metaphor

We are now in a position or a broader evaluation o the proposed strategies. o

begin with, what justifies the compositional and two-tier coding strategies in view

o their time-intensiveness? I shall argue that they make up or this drawback

through improved workflow management, methodological precision, and analytic

scope (4.1 and 4.2). Te subsequent subsections will illustrate the rich analytic

possibilities or reconstructing metaphor cohesion, narrative linkages, and or

multi-method comparisons (4.3–4.5).

. Requirements o a good coding strategy

Annotation is beset by two interconnected problems: First, we need to find code

names that capture what the expressions subsumed share. Second, we need to de-

cide on a general strategy, i.e. a coding “design” that is calibrated to the aimed at

analysis (see 2.3). Let us briefly contrast the two available main options here. Te

most intuitive strategy is to assign a ull mapping ormula to a metaphor the mo-

ment we hit upon it. Tis one metaphor-one code procedure will later make each

type o mapping retrievable by clicking a code. We may call it “one-shot coding”.

By contrast, compositional coding, the strategy I recommend, applies two codes to

each metaphor and uses a co-occurrence search to retrieve the mapping later on.

Is this slightly roundabout strategy really preerable to the simplicity o one-shot

coding? Although the latter works well enough or small corpora, compositional

coding gains appeal with all larger and metaphorically diverse corpora or three

reasons. One has to do with code list size, one with workflow, and a third one with

the superior possibilities in the later analysis.

Te first drawback occurs when many new metaphorical phenomena keep

popping up. In one-shot coding this makes the code list explode in size. One may

have to add new ormulas ar beyond the 20–30% o a corpus considered sen-sible. In compositional coding many combinatorial possibilities can be generated

out o a circumscribed set o basic codes. Second, one-shot coding tends to orce

researchers to repeatedly adapt a code name to make it an adequate “summary”


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Michael Kimmel

o what is presently in the category, as we usually base the decision whether a

text unit fits on code nomenclature. With one-shot coding reormulations tend

to occur way into the project, necessitating requent re-checks o all earlier codedexpressions. Making a choice between two hal-fitting mappings or finding the

right level o abstraction can also be extremely laborious. All this impedes the

workflow. By contrast, compositional coding deers the difficulty o finding or-

mulas until later and thereby rees the mind. Afer the coding stage the researcher

will have a synoptic overview o the corpus and can use co-occurrence tables to

define the actual common denominator or a ormula. Tus, while speed is sacri-

ficed by tagging every metaphor at least twice, time is gained elsewhere. What is

more, one-shot ormulas risk a certain bias, especially or inexperienced research-

ers. In actual metaphor usage not all aspects o a source domain are mapped onto

a targe

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