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How to Lose Cases and Influence People

  • Rachael K. Hinkle EMAIL logo and Michael J. Nelson ORCID logo
Published/Copyright: March 10, 2018
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Statistics, Politics and Policy
From the journal Statistics, Politics and Policy Volume 8 Issue 2

Abstract

Dissenting opinions are common in the US Supreme Court even though they take time and effort, risk infuriating colleagues, and have no precedential value. In spite of these drawbacks, dissents can potentially contribute to future legal development. We theorize that dissenting justices who use more memorable language are more successful in achieving such long-term impact. To test this theory, we amass an original dataset of citations to dissenting opinions extracted directly from majority opinion text. We further leverage these texts to build an algorithm that quantifies the distinctiveness of dissenting language within a dynamic context. Our results indicate that dissents using more negative emotion and more distinctive words are cited more in future majority opinions. These results contribute to our understanding of how language can influence long-term policy development.

Acknowledgments

We thank Steven Morgan and Rachel Fleisher for research assistance and Robert Hume for helpful comments.

Appendix A

Table 3:

Word Stems Excluded from LIWC Emotion Categories.

Negative emotionPositive emotion
abandon*accept
advers*accepta*
aggravat*Accepted
argu*Accepting
assault*accepts
careless*agree
complain*attachment*
defect*award*
defenc*benefit
defens*care
gross*convinc*
harass*faith*
hostil*forgiv*
neglect*free*
painprofit*
prejudic*satisf*
resign*secur*
victim*trust*
value
valued
values
valuing

  1. *All words with these stems and various suffixes were counted.

Table 4:

Modified LIWC Positive Emotion Dictionary.

active*cherish*enjoy*gorgeous*importan*okayrich*terrific*
admir*chuckl*entertain*graceimpress*okaysroflthank
ador*clever*enthus*gracedimprove*oksromanc*thanked
advantag*comed*excel*graceful*improvingopenminded*romantic*thankf*
adventur*comfort*excit*gracesincentive*opennesssafe*thanks
affection*commitment*fabgraci*innocen*opport*savethoughtful*
agreeab*compassion*fabulous*grandinspir*optimal*scrumptious*thrill*
agreedcompliment*fantastic*grande*intell*optimi*sentimental*toleran*
agreeingconfidencefavor*gratef*interest*originalsharetranquil*
agreement*confidentfavour*grati*invigor*outgoingsharedtreasur*
agreesconfidentlyfearless*greatjoke*painl*sharestreat
alright*consideratefestiv*grinjokingpalatabl*sharingtriumph*
amaz*contented*fiesta*grinn*joll*paradisesilli*true
amor*contentmentfinegrinsjoy*partie*sillytrueness
amus*coolflatter*hakeen*party*sincer*truer
aokcourag*flawless*haha*kiddingpassion*smart*truest
appreciat*create*flexib*handsom*kindlypeace*smil*truly
assur*creati*flirt*happi*kindn*perfect*sociab*truth*
attract*credit*fondhappykiss*playsoulmate*useful*
awesomecute*fondlyharmless*laidbackplayedspecialvaluabl*
beaut*cutie*fondnessharmon*laugh*playful*splend*vigor*
beloveddaringforgaveheartfeltlibert*playingstrength*vigour*
benefic*darlin*freeheartwarm*likeab*playsstrong*virtue*
benefitdear*freeb*heaven*likedpleasant*succeed*virtuo*
benefitt*definitefreed*heh*likesplease*success*vital*
benevolen*definitelyfreeinghelper*likingpleasingsunnierwarm*
benign*delectabl*freelyhelpful*livel*pleasur*sunniestwealth*
bestdelicate*freenesshelpinglmaopopular*sunnywelcom*
betterdelicious*freerhelpslolpositiv*sunshin*well
bless*deligh*frees*hero*loveprais*superwin
bold*determina*friend*hilariouslovedprecious*superior*winn*
bonus*determinedfunhoho*lovelyprettie*supportwins
brave*devot*funn*honest*lover*prettysupportedwisdom
bright*digni*genero*honor*lovespridesupporter*wise*
brillian*divin*gentlehonour*loving*privileg*supportingwon
calm*dynam*gentlerhopeloyal*prize*supportive*wonderf*
caredeager*gentlesthopedluckpromis*supportsworship*
carefreeease*gentlyhopefulluckedproud*suprem*worthwhile
careful*easie*giggl*hopefullylucki*radian*sure*wow*
careseasilygiver*hopefulnesslucksreadinesssurpris*yay
caringeasinessgivinghopesluckyreadysweetyays
casualeasinggladhopingmadlyreassur*sweetheart*
casuallyeasy*gladlyhugmagnific*relax*sweetie*
certain*ecsta*glamor*hugg*merit*reliefsweetly
challeng*efficien*glamour*hugsmerr*reliev*sweetness*
champ*elegan*glori*humor*neat*resolv*sweets
charit*encourag*gloryhumour*nice*respecttalent*
charm*energ*goodhurra*nurtur*revigor*tehe
cheer*engag*goodnessideal*okreward*tender*
Table 5:

Modified LIWC Negative Emotion Dictionary.

abuse*critici*dwell*griev*kill*numb*rude*stubborn*uneas*
abusi*crude*egotis*grim*lame*obnoxious*ruin*stunkunfortunate*
ache*cruel*embarrass*grouch*lazie*obsess*sadstunnedunfriendly
achingcrushedemotionalgrr*lazyoffence*sadde*stunsungrateful*
afraidcryempt*guilt*liabilit*offend*sadlystupid*unhapp*
aggress*cryingenemie*harmliar*offens*sadnessstutter*unimportant
agitat*cunt*enemy*harmedliedoutrag*sarcas*submissive*unimpress*
agoniz*cutenrag*harmful*liesoverwhelm*savage*suckunkind
agonycynicenvie*harminglone*painedscare*suckedunlov*
alarm*damag*enviousharmslonging*painf*scaringsucker*unpleasant
alonedamn*envy*hatelosepainingscarysucksunprotected
anger*danger*evil*hatedloser*painssceptic*suckyunsavo*
angr*daze*excruciat*hateful*losespanic*scream*sufferunsuccessful*
anguish*decay*exhaust*hater*losingparanoi*screw*sufferedunsure*
annoy*defeat*fail*hatesloss*pathetic*selfish*sufferer*unwelcom*
antagoni*degrad*fakehatinglostpeculiar*serioussufferingupset*
anxi*depress*fatal*hatredlous*perver*seriouslysuffersuptight*
apath*depriv*fatigu*heartbreak*low*pessimis*seriousnesssuspicio*useless*
appall*despair*fault*heartbroke*luckless*petrif*severe*tantrum*vain
apprehens*desperat*fearheartless*ludicrous*pettie*shake*tearsvanity
argh*despis*fearedhelllyingpetty*shaki*teas*vicious*
arrogan*destroy*fearful*hellishmadphobi*shakytempervile
asham*destruct*fearinghelpless*maddeningpiss*shame*tempersvillain*
asshole*devastat*fearshesita*madderpiti*shit*tense*violat*
attack*devil*feroc*hitmaddestpity*shock*tensingviolent*
aversi*difficult*feud*homesick*maniac*poison*shooktension*vulnerab*
avoid*disadvantage*fieryhopeless*masochis*pressur*shy*terribl*vulture*
awfuldisagree*fight*horr*melanchol*prick*sicken*terrifiedwar
awkward*disappoint*firedhumiliat*messproblem*sinterrifieswarfare*
baddisaster*flunk*hurt*messyprotestsinisterterrifywarred
bashful*discomfort*foe*idiot*miser*protestedsinsterrifyingwarring
bastard*discourag*fool*ignor*missprotestingskeptic*terror*wars
battl*disgust*forbid*immoral*missedpuk*slut*thiefweak*
beatendishearten*foughtimpatien*missespunish*smother*thieve*weapon*
bitch*disillusion*frantic*impersonalmissingrage*smug*threat*weep*
bitter*dislikefreak*impolite*mistak*ragingsnob*tickedweird*
blam*dislikedfright*inadequa*mockrancid*sobtimid*wept
bore*dislikesfrustrat*indecis*mockedrape*sobbedtortur*whine*
boringdislikingfuckineffect*mocker*rapingsobbingtough*whining
bother*dismay*fucked*inferior*mockingrapist*sobstraged*whore*
brokedissatisf*fucker*inhib*mocksrebel*solemn*tragic*wicked*
brutal*distract*fuckin*insecur*molest*reek*sorrow*trauma*wimp*
burden*distraughtfucksinsincer*mooch*regret*sorrytrembl*witch
cheat*distress*fume*insult*moodi*reject*spite*trick*woe*
confront*distrust*fuminginterrup*moodyreluctan*stammer*triteworr*
confus*disturb*furious*intimidat*moron*remorse*stanktrivi*worse*
contempt*domina*furyirrational*mourn*repress*startl*troubl*worst
contradic*doom*geek*irrita*murder*resent*steal*turmoilworthless*
crapdork*gloom*isolat*nag*restless*stench*ughwrong*
crappydoubt*goddam*jadednast*revenge*stink*ugl*yearn*
craz*dread*gossip*jealous*needyridicul*strain*unattractive
crieddull*grave*jerknerd*rigid*strangeuncertain*
criesdumb*greed*jerkednervous*risk*stress*uncomfortabl*
criticaldump*griefjerksneurotic*rottenstruggl*uncontrol*
Table 6:

The change in the predicted probability of a non-negative citation from Model 1 when moving each dichotomous variable from 0 to 1 and moving each continuous variable from its 25th percentile to its 75th percentile (while the age of the dissent is fixed at nine, the median age of a cited dissent, and all other variables are held at their median).

Change95% CI
Negative emotion words0.0005[0.0002, 0.0008]
Positive emotion words0.0004[−0.0001, 0.0008]
Distinctive words0.0005[0.0001, 0.0009]
“I Dissent”−0.0007[−0.0015, 0.0002]
First person pronouns0.0001[−0.0002, 0.0004]
Case specific language−0.0011[−0.0018, −0.0004]
ln(Word count)0.0051[0.0037, 0.0066]
3 Dissenting votes (compared to 4)−0.0006[−0.0014, 0.0002]
2 Dissenting votes (compared to 4)−0.0016[−0.0026, −0.0006]
1 Dissenting votes (compared to 4)−0.0026[−0.0037, −0.0014]
Ideological distance−0.0015[−0.0022, −0.0007]
Prop. of same issue on docket0.0007[0.0001, 0.0013]
Age of dissent−0.0028[−0.0037, −0.0020]

  1. The relevant 95% confidence interval appears in brackets next to each predicted change.

Appendix B: Coding Rules to Identify Negative Citations

A majority opinion’s citation to a dissent was classified as a negative citation (and therefore excluded from our analysis) if, and only if, one of the three following criteria were met:

  1. The majority opinion cited a dissent in order to explicitly reject its reasoning.

  2. The majority opinion only referenced the dissent to note that its reasoning had been rejected by a majority of the Court in the past.

  3. The citation was prefaced with a “but see” signal (which indicates a source directly contradicts a stated proposition, see The Bluebook: A Uniform System of Legal Citation, Rule 1.2).

Only 6.1% of citations in our dataset fit one of these three criteria.

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Article note:

Replication files are available at http://rachaelkhinkle.com/research.html.

Published Online: 2018-3-10
Published in Print: 2017-12-20

©2017 Walter de Gruyter GmbH, Berlin/Boston

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  2. Editor’s Note
  3. Editor’s Note
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https://doi.org/10.1515/spp-2017-0013

Articles in the same Issue

  1. Frontmatter
  2. Editor’s Note
  3. Editor’s Note
  4. Articles
  5. Unpacking Big Data in Education. A Research Framework
  6. A Semi-automatic Method to Retrieve Twitter Accounts
  7. Themes and Topics in Parliamentary Oversight Hearings: A New Direction in Textual Data Analysis
  8. How to Lose Cases and Influence People
  9. Policy and Voting
  10. Examining the Policy Learning Dynamics of Atypical Policies with an Application to State Preemption of Local Dog Laws
  11. Voting in Nigeria: Determinants of Turnout in the 2015 Presidential Election
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