writing papers going to conferences 09-10

8/11/2019 Writing Papers Going to Conferences 09-10

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WRITING PAPERSAND GOING TO

CONFERENCES

Gita Subrahmanyam

Authoring a PhD and Developing

as a Researcher


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OUTLINE OF WORKSHOP

Why go to conferences and seminars?

Hierarchy of conferences

Writing, structuring and proposing

papers

Delivering papers


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WHY ARE YOU HERE TODAY?

As a table, talk about your experiences to

date

Have you been to a conference? As anattendee or as a paper-giver?

Do you have a conference coming up that

you would like help with? Why are you here today? What do you

hope to get out of todays workshop?


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Credit:www.imageafter.com

Transmittingideas is a keystep in gettingfeedback andupgradingyour

knowledge.


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WHY GO TO SEMINARS ANDCONFERENCES? 1. For staff

Create deadlines using short papers tokick-start your publications

Meet collaborators, friends, age cohort

Plug into the wider profession and gainan understanding of fashions, trends,tribes, taboos, discourses - and wherethe LSE sits

Collate oral wisdoms, gossip, tips

Book exhibitions, meet with publishers,network at dinners, receptions, bars


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HIERARCHY OF CONFERENCES

Seminars in home institution - knownaudience

Postgraduate conferences

External seminars, specialist groups inyour profession (wider audience)

UK national conferencechoice of panels

European-level international conferencesworkshops, panels, specialist groups

US/global conferenceshuge attendancebut often tiny audiences at individualpanelsreal action in bars, book fairs,receptions


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THE ELEMENTS OF A

GOODPAPER AND

PROPOSAL


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FOCUS ON THENEED TO KNOW CRITERION

Normal (written) form is:

What do readers really need to know?

Conference (presentation) form is:

What does the audience really need

to see on screen?

What do listeners really need to haveexplained to them?


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NEED TO KNOWIMPLICATIONS 1

However literary your normal style, plan

the talk as a sequence of exhibits

Put all that you want to say/show onscreen, in a user-friendly manner

Practice timings for your talk

Aim for a fast, well-paced startdo notwarm up the audience to your subject

Sellthe paperdont be diffident


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Organise your talk into 3 minute chunks,planning for onedisplay per chunk

Use PowerPoint (not Word) to composeyour displaysand have OHP backups!

Text should be free-standing and readilyunderstandable without you speaking

(audience will deconstruct it like that) Try to avoid a build-up of slides or too

many flying bullets delays expositionand too controlling


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Pick a font that is visible to someone in

the back row - like this one

Put equations and quantitative tablesinto separate image screens, magnified

so that the smallest subscript is visible

Preferably use summary data tables,rather than detailed ones

Pick the best feasible fonts for display


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TIME LIMITS FOR PRESENTATIONS

Seminars ... 30 to 40 minutes UK and most European conferences -

20 minutes per paper, then questions;

normally 2 or 3 papers per panel US and most international conferences -

10 to 15 minutes per paper, followed byquestions; often 4 or 5 papers per panel

Workshops and intensive conferences20-30 minutes per paper, followed byone-hour discussion time


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IMPLICATIONS FOR PROPOSALS

A conference proposal/abstract shouldbe an accurate and concise summary of

what the paper delivers

Check the Call for Papers carefully What are the key themes of the

conference?

What kind of presentationwill you do? How long should the abstract be?

When is the deadline for submission?
http://www.hicsocial.org/cfp_ss.htmhttp://www.hicsocial.org/cfp_ss.htm


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IMPLICATIONS FOR PROPOSALS (2)

Need to know criterion should guide

abstract

What do organisers need to know to

assess whether to accept the paper andwhere to place it in a panel?

Core argument/bottom-line findings

should form centre-piece of the abstract Dont waste words on literature review

or methodology


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HAVE A GO

Write a proposal/abstract for theconference of your choice

Follow the Call for Papers guidelines inthe example you brought in, EXCEPTstick to a maximum of 200 words

If you havent brought a Call forPapers, then try using one of the sparecopies at the front of the room


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A GOODPROPOSAL/ABSTRACT

Sentence 1a hook, indication of

motivation (for you and reader)

Sentences 23formulation ofresearch problem/question

Sentences 34outline of core finding

(maybe a sideways glance at method) Sentences 56 - implications


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GET SOME FEEDBACK

Pass your abstract to the person onyour left

Read the abstract you have in front ofyou and think about what you might doto improve it

Feed back to the person who handedyou their abstract, and get feedback onyour own abstract


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WHAT CAN GO WRONG

ON THE DAY WITH AN

OTHERWISE GOOD SEMINAROR CONFERENCE PAPER


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SCARY CONFERENCE VISION

- real life is more prosaic


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BE PREPARED FOR A REALISTIC

AUDIENCE SIZE

Check the venue in advance for size and

featuresmay indicate audience size

Conference slots respond to multiple factors,

including competition, timings etc

so dont regard small audiences,

dribbling in late, in an over-large room, as

unusual or depressing Alternatively beware of an over-large

audience, cramped and uncomfortable in too

small a room


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BE PREPARED FOR POSSIBLE

PRESENTATION PROBLEMS

Presentation facilities vary unpredictably -

you need to be adaptable

Take Powerpoint slides in two storage

formats (e.g. USB stick and CD).

Email slides to seminar hosts.

Take an OHP copy of slides

Print readable handout copies of slides

for a realistic audience (say 25)

Take 10-15 full paper copies, for zealots


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THINGS TO AVOID, IDEALLY:- BEING INVISIBLEby never standing up- HAVE NO VISUALS AIDSunexciting

- READING THE PAPER WORD FOR WORD

http://www.mcdonald.cam.ac.uk/McD/Seminar.jpg


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THINGS TO AVOID, contd.USING BADLY CONSIDERED VISUALSthat are unreadable and do not project

well on an OHP (or in PowerPoint)


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PLAN FOR POSSIBILITY THATYOU MAY BE ALLOCATED A

NOT-SO-IDEALROOM AND THINK ABOUT

HOW TO ADJUST FOR IT


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Credit: http://www.finearts.uvic.ca/visualarts/facilities/images/seminar/seminar-1.jpg

RANDOM UNIVERSITY ROOMfunctional but depressing, no

daylight, blackboard!


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CREDFIT: http://www.eastwood.asn.au/images/hall15_b.jpg

SMALL ROOM HAZARDSnoOHP, no screen, table dominating

the space,.. + dogs!


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Credit: http://www.brc.ubc.ca/vtour/images/cell/L3_seminar1.jpg

LARGE ROOM HAZARDSlong thinroom, audience obstructs each othersview, no equipment for visual displays


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https://reader010.{domain}/reader010/html5/0613/5b20b4cc6a027/5b20b4dc752a9.jpg

SUBTLE HAZARDS - half the audiencecant see the OHP, narrow tables, and

uncomfortable seating arrangment


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Credit: http://www.ruwpa.st-and.ac.uk/workshop2002/seminar%2520room3.jpg

THINGS TO AIM FOR, IDEALLYSTAND UP, and use CLEAR, VARIED

SLIDES for best feasible delivery


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https://reader010.{domain}/reader010/html5/0613/5b20b4cc6a027/5b20b4ddcd

THINGS TO AIM FOR, contdFOR LARGE AUDIENCES (just in case)

think of the view from the back row

IDEAL SEMINAR ROOM


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Credit: http://www.reidkerr.ac.uk/conference/images/ante2B.jpg

IDEAL SEMINAR ROOMcentraldisplay screen + OHP, wide tables,space for moving around, daylight or

good lighting, smallish group


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PRESENTING DATA

poor y


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INDIVIDUAL AND BLOC INCENTIVES UNDERWEIGHTED VOTING *

Patrick Dunleavy and Rolf Hoijer

LSE Public Policy Group,London School of Economics and Political Science,

Houghton Street,London, WC2A 2AE

Abstract: Pioneering work by Laver and Benoit (LB) argues that a drive by individuallegislators to maximize their per capita Shapley-Shubik power scores could explain theevolution of party systems in legislatures. But LBs analysis exhibits several problems.Theoretically their utility premises are incompletely specified and would lead tosystematically irrational and short-termist behaviour by members of vote blocs.Methodologically LB focus on a complex ratio variable, whose patterning essentially dependson another largely unanalysed variable, the power index scores of whole vote blocs. LB haveno framework for economically analysing variations in power index scores across verynumerous and diverse voting situations. Empirically LBs account radically mis-specifies thefactors conditioning blocs incentives or actors incentives. We show that: (i) they offer anexaggerated picture of the scope for defection; and (ii) their emphasis on the importance ofdominant bloc status for the largest bloc is incorrect - dominance is often empirically trivialin shaping bloc scores when there are more than five blocs. Instead, the factors that do

influence blocs scores are predictable, (if complex), patterns, which repeat in recognizableways across weighted voting situations, for any given threshold level. We demonstrate amethod for mapping these scores comprehensively and economically, and for analysinginfluences on the scores precisely.

Paper to the panel on New Perspectives on Rights, Freedoms, and Powers at the EuropeanConsortium of Political Research, Annual Workshops 2003, University of Edinburgh, 28March 2 April 2003.

STARTBADLYIve printedmy cover

page in tinyfont andslapped iton the OHP

slide


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analysis, and his lonely faith in the value of other effective number indices, for which there

has been little or no take-up in the existing literature. By contrast we believe that the wider

effective number family has little to offer, and that continuing to use unmodified N 2in

particular in quantitative applications cannot be defended because of the defects set out

here.

In our view averaging N2scores with the 1/V1score creates a simple but useful

variant of the effective number index, Nb:

The data demands of equation (3) are no greater than for the N2index, and Nband N2are

highly correlated with each other. Yet this straightforward modification has useful effects.

Figure 6 shows the minimum and maximum fragmentation lines for Nbwith between 2 and

8 parties, and also includes the 1/V1line and the overall maximum fragmentation line for

Nb(with a 1 per cent floor for party sizes, as before). The averaging of N2and 1/V1

creates much less curved minimum fragmentation lines. And although there are still

transitions in their slopes around the anchor points, they are much less sharp than with N 2.

The maximum fragmentation lines for different relevant numbers of parties are also

considerably straightened out under Nb, without strongly visible curves close to theirterminal anchor points. The overall maximum fragmentation line for Nbis appreciably

lower than the 1/V12line under N2. In fact the Nbmaximum fragmentation line runs quite

close to but slightly above the N3maximum line shown in Figure 1. For instance, with V1

at 60 per cent, the maximum Nbscore is more than half a party less than with N2 ; and at

50 per cent support the Nbupper limit is 3 parties, instead of 4 for N2. Thus the Nbindex

delivers many of the same benefits in terms of more realistically denominated scores as N3,

but it avoids N3s severe kinks around anchor points (which is evident in Figure 4).

Table 2 shows how the N2, Nband Molinar measures behave empirically across the

(3)

MAINTAINCONSIS-TENCY:Some ofyou maynot be ableto see thesubscriptshere toowell


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Figure 7.1: How health boards compare

Trtmnt rates/pop

Argyll & Clyde 33212.42

Ayrshire &

Arran

33200.32

Border 72331.011

Dumfries &

Galloway

31699.21

Fife 22876.55

Forth Valley 29748.33

Grampian 27681.49

Greater

Glasgow

31827.222

Highland 33855.18

Lanarkshire 23909.83

Lothian 31768.41

Orkney 21727.37

Shetland 28233.25

Tayside 50259.21

Western Isles 30840.19

1Includes Berwick in 1997-98 only

. 2.Estimates only due to data problems

.

TABLEScomplex,difficult toread, weakheading/title,

unnecessaryabbreviations,space wastedbetween data

points


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Key: The health boards are as follows: 1 Ayre & Clyde; 2 Ayrshire & Arran; 3 Border; 4 Dumfries &

Galloway; 5 Fife; 6 Forth Valley; 7 Grampian; 8 Greater Glasgow; 9 Highland; 10 Lanarkshire; 11 Lothian;

12 Orkney; 13 Shetland; 14 Tayside; 15 Western Isles.

FIGURE 7.4: HOW HEALTH BOARDS COMPARE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0

1 0 0 0 0

2 0 0 0 0

3 0 0 0 0

4 0 0 0 0

5 0 0 0 0

6 0 0 0 0

7 0 0 0 0

8 0 0 0 0

Trtmnt rates/pop

CHARTS3D design,small and thin,weak heading,no logic to

arrangementof bars, labelsin a legend,key details in

micro font


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Table 5: The extreme bloc sizes and per capita SS values in the triads, quinns and sevensareas

i. Triads area

Bloc sizes Per capita SS scores

Description Blocs V 1 V2 V3 V1 V2 V3 Diff

Bottom left cell All 26 26 25 1.28 1.28 1.33 0.05

Bottom right cell 4 48 0.69 0.64

8 44 0.76 0.57

14 38 26 25 0.88 1.28 1.22 0.4520 32 1.0 0.33

24 28 1.2 0.13

26 26 26 25 1.28 1.28 1.33 0.05

Top right cell 4 48 48 3 0.69 0.69 11.11 10.42

8 44 44 7 0.76 0.76 4.76 4.0

14 38 38 13 0.88 0.88 2.38 1.4

20 32 32 19 1.0 1.0 1.67 0.67

24 28 28 23 1.2 1.2 1.39 0.19

26 26 26 25 1.28 1.28 1.33 0.05

ii. Quinns area


Description Blocs V 1 V2-V4 V5 V 1 V2-V4 V5 Diff

Bottom left cell All 17 17 17 1.18 1.18 1.18 0

Bottom right cell 6 31 0.65 0.53

8 29 17 17 0.69 1.18 1.18 0.49

14 23 0.87 0.45

20 17 17 17 1.18 1.18 1.18 0

Top cell 6 24 24 3 0.69 0.69 6.67 5.98

8 23 23 5 0.76 0.76 4.0 3.24

14 20 20 11 1.0 1.0 1.82 0.1820 17 17 17 1.18 1.18 1.18 0

iii. Sevens area


Description Blocs V 1 V2-V4 V5-V6 V7 V 1 V2-V4 V5-V6 V7 Diff.Bottom left cell All 13 13 13 13 1.10 1.10 1.10 0

Bottom right cell 6 21 13 13 13 0.68 1.10 1.10 1.10 0.42

8 15 13 13 13 0.95 1.10 1.10 1.10 0.15

14 13 13 13 13 1.10 1.10 1.10 1.10 0

Top cell 6 16 16 13 9 0.89 0.89 1.10 1.59 0.70

8 14 14 13 11 1.02 1.02 1.10 1.30 0.28

14 13 13 13 13 1.10 1.10 1.10 1.10 0

VERY

LARGETABLESmultiplesmudges ofmicro font arenot ideal forpresenting fullregression

results to acrowded room


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PRESENTING DATA

prop r y

STRONG EXPOSITION


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Credit: http://www.pi1.physik.uni-stuttgart.de/Soellerhaus2002/Bilder/Soellerhaus2002-12.jpg

STRONG EXPOSITIONproperdisplay, visible fonts, speaker visibleand using pointer for details


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Formula for effective number

of parties

Treatment rates


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Health boardsTreatment rates

per 100,000

people

Border 723 Upper outlier

Tayside 503 Upper outlier

Highland 339

Ayrshire andArran

332 Upper quartile

Argyll and Clyde 332

Lothian 318

Greater Glasgow 318

Dumfries andGalloway

317 Median

Western Isles 308

Forth Valley 297

Shetland 282

Grampian 277 Lower quartile

Lanarkshire 239

Fife 229

Orkney 217

Mean treatment rate 335

Figure 7.2: How

Scotlands health

boards comparedin treating

cataracts, 1998-9

financial year

Notes:Treatment rates per

100,000 peopleThe range is 506, and the

midspread (dQ) is 55.

Source: National Audit

Office, 1999.

Figure 1: How Scottish health boards treat


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0 100 200 300 400 500 600 700

Orkney

Fife

LanarkshireGrampian

Shetland

Forth Valley

Western Isles

Dumfries and Galloway

Greater Glasgow

Lothian

Argyll and Clyde

Ayrshire and Arran

Highland

Tayside

Border

Figure 1: How Scottish health boards treat

cataracts, 1999-2000

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