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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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NEED TO KNOWIMPLICATIONS 2
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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NEED TO KNOWIMPLICATIONS 3
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
Bloc sizes Per capita SS scores
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
Bloc sizes Per capita SS scores
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