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Enregistrement W4402712075 · doi:10.17605/osf.io/hku5e

Speaking like ordinary people, representing ordinary people?

2025· article· en· W4402712075 sur OpenAlexaff
Philippe Chassé

Notice bibliographique

RevueHAL (Le Centre pour la Communication Scientifique Directe) · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiquePopulism, Right-Wing Movements
Établissements canadiensCollège Lionel Groulx
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

The rise of populism in Western democracies and beyond has garnered significant attention from researchers. While the prevailing view in the literature conceptualizes populism as a “thin ideology” (Mudde 2004), other scholars contend that it is better understood as a “style of political communication” (e.g., Jagers and Walgrave 2007; Moffitt 2016; Ostiguy and Moffitt 2021). According to these researchers, a defining characteristic of populist politicians is the way they speak. Not only would they be inclined to adopt rhetoric that pits virtuous citizens against corrupt elites, but they would also tend to use simple, informal language (Moffitt and Tormey 2014). This style would reflect a desire to connect with “ordinary” people and to set themselves apart from conventional politicians. Yet, while numerous studies have examined differences in how populist politicians communicate, we still know little about citizens’ attitudes toward the language style of political candidates. Do voters prefer politicians to speak more casually, with simple words and phrases, or do they expect those seeking office to use more formal language? Do all voters share the same expectations regarding how political candidates express themselves? So far, the emerging literature on this topic has focused primarily on the effect of language complexity on voter attitudes. Bischof and Senninger (2024) demonstrate that the level of complexity of a political candidate’s language affects how citizens perceive their socioeconomic status. Kittel (2024), on the other hand, finds that German voters appear less inclined to support candidates who use simple language compared to those who use average complexity language. However, these studies exclusively explore evaluations of written content and thus cannot address how citizens respond when they hear candidates speak. It seems essential to investigate perceptions of spoken language, as the criteria for evaluating oral communication differ from those used for written content, and citizens are far more likely to hear candidates than to read them. This article will examine how language-based judgments shape the public image of political figures. Using two randomized survey experiments conducted in the United States of America, I will analyze the effects of candidates’ language styles on voter attitudes. Unlike previous research centered on the evaluation of written content, I will focus on language registers. Though registers are inherently connected to the level of complexity of spoken or written productions, they carry a more significant political dimension, as they account for the level of formality in communication and are generally associated with specific socioeconomic groups. The first study will examine the effect of language register when political candidates make non-controversial statements, while the second study will focus on the effect of register when candidates use populist rhetoric. The rationale for conducting two studies is to determine whether language register influences citizens’ attitudes independently of the type of message conveyed by the candidates. The first study constitutes a more isolated test, in which only variation in register can be associated with the populist style. The second, by contrast, serves as a more conservative test, wherein two features potentially linked to populism—register and message content—are manipulated simultaneously. In both studies, brief audio recordings representing different experimental conditions will be randomly assigned to respondents. The level of formality—and consequently, the level of complexity—of the scripts of the speeches will vary from one recording to another. After listening, respondents will rate the candidates on various personal qualities and indicate whether they feel the candidate can understand their concerns and represent their interests. They will also assess the likelihood that they would vote for the candidate.

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.

Comment cette classification a été obtenuedéplier

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)0,008
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,636
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0020,000
Communication savante0,0010,001
Science ouverte0,0020,001
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 Opus0,013
Tête enseignante GPT0,269
Écart entre enseignants0,257 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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