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Enregistrement W2542045846

The Gender Gap in Political Knowledge in Poland

2016· article· en· W2542045846 sur OpenAlexaboutno aff
Robert M. Kunovich, Sheri Kunovich

Notice bibliographique

RevuePolish Sociological Review · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueGender Politics and Representation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPoliticsPolitical communicationDemocracySociologyPolitical culturePolitical scienceSocial scienceLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

IntroductionThere is a great deal of interest and research on gender gap in political knowledge. Three basic questions often frame this literature: Is there a gender gap in political knowledge, why is there a gap, and are sources of political knowledge same for men and women? Research suggests that there is often a gender gap in political knowledge. Scholars disagree about its source. For some, it is a methodological artifact (e.g., Mondak and Anderson 2004). For others, it reflects differences in characteristics, such as level of interest in politics, or differences in return for characteristics, such as education (Dow 2009). Most of literature on political knowledge, however, focuses on US. We seek to add to literature on political knowledge by addressing following questions: What are sources of political knowledge and are they same for men and women in Poland, is there a gender gap in political knowledge in Poland, and are patterns found in a consolidating democracy similar to those found in established democracies?We use nationally representative survey data to examine how motivation, ability, and opportunity influence men's and women's knowledge of twelve national political parties- that is, whether they could correctly indicate if each party was currently in ruling coalition. We predict whether or not respondents answer 'don't know' to entire question set as well as whether or not they were able to answer all twelve questions correctly. Independent variables include political interest (motivation); educational attainment and cognitive ability (ability); household income, access to cable or satellite TV, internet access, voting experience, employment status, religious attendance, size of place of residence, marital status, and having children (opportunity); and controls (age and self-esteem). We use multiple imputation to handle missing data and estimate interaction models to test for differences in coefficients for women and men.This paper makes several contributions to literature on political knowledge. First, it examines gender gap in political knowledge in a new political context-Poland. There are a few single-country studies that focus on gender gap in political knowledge outside of US-for example, in Belgium (Hooghe, Quintelier, and Reeskens 2006), Britain (Frazer and Macdonald 2003), Canada (Stolle and Gidengil 2010), and China (Tong 2003). We hope that our analysis will help to establish whether or not differences in levels and sources of political knowledge are similar to patterns found in other countries despite differences in political context. Second, our data include measures of both educational attainment and cognitive ability and cognitive ability measure is based on an intelligence test rather than interviewer assessment. Third, we examine political knowledge as a two-stage process by first predicting whether respondents answered knowledge questions or simply indicated that they 'don't know' for entire question set. In second step, we examine differences in knowledge among only those providing 'yes' or 'no' answers for each party.Political KnowledgeDelli Carpini and Keeter (1996) define political knowledge as the range of factual information about politics that is stored in long-term memory (p. 10). The broad categories of political knowledge include: 'rules of game,' 'players,' and 'substance' (e.g., domestic politics) (Delli Carpini and Keeter 1996). Most scholars argue that motivation, ability, and opportunity explain why some people know more about politics than others (see Delli Carpini and Keeter 1996, Chapter 5; Dow 2009: 120; Luskin 1990: 334).First and foremost, political knowledge depends on motivation. Without interest in politics, people would not pay attention to politics nor would they retain any political information. The level of political knowledge is also rooted in ability. …

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)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,852
Score d'incertitude au seuil0,248

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
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 Opus0,207
Tête enseignante GPT0,457
Écart entre enseignants0,250 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
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

Citations2
Publié2016
Routes d'admission1
Résumé présentoui

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