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Enregistrement W7028059774

The Effect of Strategic Language on Perceptions of Actions and Speakers

2023· dissertation· en· W7028059774 sur OpenAlex

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Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueSwearing, Euphemism, Multilingualism
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésAction (physics)PerceptionContext (archaeology)TrustworthinessTerm (time)Work (physics)PoliticsEvent (particle physics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Describing the actions of others (or oneself) necessitates that a speaker make linguistic choices, as multiple terms can often be used to describe the same act. The present work investigates the consequences of these linguistic choices, assessing the extent to which a self-serving speaker can, through the strategic use of euphemistic (agreeable) and dysphemistic (disagreeable) terms, influence peoples’ evaluations of actions while avoiding the reputational consequences typically associated with deception. In this dissertation, I aim to better understand the antecedents and consequences of strategic language across different social and information contexts, discussing the theoretical and applied implications of the results of eight experiments (N = 4,828) within the context of prior work on linguistic manipulation and political polarization.
\n\tFirst, I demonstrate that participants’ evaluations of actions are made more favourable by language that replaces a disagreeable term (e.g., torture) with a semantically related agreeable term (e.g., enhanced interrogation) in an act’s description. Notably, providing participants with more knowledge about the actions they evaluated reduced (but did not eliminate) the persuasive influence of a speaker’s linguistic choices, suggesting that the persuasive potential of strategic language is greater when the details of an event are lacking. Even though the strategic use of euphemisms and dysphemisms affected action evaluations, participants judged both agreeable and disagreeable action descriptions as largely truthful and distinct from lies. Similarly, they viewed speakers attributed these descriptions as considerably more trustworthy and moral than liars. Taken together, the present work suggests that a strategic speaker can, through the careful use of language, shape public perception in a preferred direction while avoiding a majority of the reputational costs associated with less subtle forms of linguistic manipulation (e.g., lying).
\n\tSecond, I investigate the impact of strategic language in the context of political partisanship. Self-serving language is prevalent in the political realm, as liberals and conservatives are motivated to describe political events in a manner that supports group narratives and favourably presents the actions of co-partisans. Using a subset of liberal-biased (e.g., expand voting rights) and conservative-biased (e.g., reduce election security) terms from the aforementioned experiments, I find that partisans view speakers describing politically contentious events using ideologically-congruent language as more trustworthy, moral, and open-minded than speakers describing these same events in a non-partisan way (e.g., “relax voter ID requirements and expand mail-in voting”). Thus, in politically homogenous social networks, individuals (and organizations) may be incentivized to describe reality using ideologically-biased language. While beneficial to individuals in certain social contexts, the prevalence of partisan language may have negative consequences for society-at-large, exacerbating political polarization and hindering compromise across political divides. Support for this claim was found in the present work: When presented to political out-group members, partisan language produced negative evaluations of opposing partisans, with speakers attributed out-group language being viewed as untrustworthy, immoral, and closed-minded. Additionally, presenting Democrats and Republicans with ideologically-congruent descriptions of political events enhanced partisan disagreement and increased the ideological extremity of participants’ action evaluations. Therefore, partisan language, while praised by co-partisans, exacerbated political polarization, damaging trust and amplifying disagreements between Democrats and Republicans.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,213
Score d'incertitude au seuil0,833

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,297
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle