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Enregistrement W4244116186 · doi:10.1093/acrefore/9780190228637.013.641

Morality Policy

2019· reference-entry· en· W4244116186 sur OpenAlexaboutno aff
Eva‐Maria Euchner

Notice bibliographique

RevueOxford Research Encyclopedia of Politics · 2019
Typereference-entry
Langueen
DomaineSocial Sciences
ThématiqueReligion and Society Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMoralityLegalizationPornographyPoliticsSociologyValue (mathematics)Political scienceLaw

Résumé

récupéré en direct d'OpenAlex

Morality policies are a specific set of public issues that provoke fierce debates over the “right way” of living. Popular examples are the referendum on same-sex marriage in Ireland in 2015, the conflict on abortion policy in Poland in 2016, the reform on prostitution policy in France in 2016, and the legalization of assisted dying in Canada in 2016. Future moral questions concern the use of CRISPR in gene editing of embryos, transgender rights, the regulation of self-driving cars with a hands-off regulation, and the involvement of robots in elderly care. Morality policy analysis is a relatively new field of study that struggles with finding a clear-cut definition and delimitation of morality issues from nonmorality issues. The lowest common denominator is that value conflicts over “first principles” and “battles between right and wrong” are indicative of this type of policy, while monetary values fade into the background. Based on this definition, four groups of typical value-loaded topics can be identified, issues related to: life and death (e.g., assisted dying, abortion policy, artificial reproduction, capital punishment), gender and sexuality (e.g., homosexuality, prostitution, pornography, sex education, transgender rights), addictive behavior (e.g., drug policy, gambling policy), and limitations on individual self-determination (e.g., gun policy, veil policy, Islamic religious education). The basic analytical question that drives the scholarly community is the popular proposition that “policies determine politics.” In other words, the underlying key interest is whether morality policies provoke different political processes than “nonmorality” issues. At first, scholars from the United States started to explore this question, which was also known as “culture wars.” Later on, since the early 2000s, the enquiry expanded in Europe. Thus, a growing number of researchers are investigating policymaking processes for morality issues and are evaluating traditional explanatory factors from the field of comparative public policy analysis. These factors include, among others, the influence of political parties and party cleavage structures, interest groups and societal mobilization, and institutional as well as cultural variables (e.g., religion, value change, and cultural modernization). In most cases, a uniform and direct impact of these factors is controversial, which is probably related to disagreement about the classification of public issues as moral problems. Discussion of this problem would benefit from contributions from other fields, such as research on religion and politics, the literature on gender and politics, legislative behavior, and political psychology. Aside from a more careful review of traditional explanations of morality policy change, including in particular the role of political institutions, it would be enriching to widen the analytical focus and investigate other stages of the policy cycle. The implementation phase is particularly interesting because morality policy outputs often suffer from legal vagueness, which leaves wide room for discretion by street-level bureaucrats or other third parties. Moreover, an increasing number of cross-policy comparisons (including comparisons between morality and nonmorality issues), as well as an alternative set of methodological tools (e.g., social experiments, network analysis, and quantitative content analysis), would enrich our understanding of morality policymaking.

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)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,470
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,097
Tête enseignante GPT0,457
Écart entre enseignants0,360 · 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
GenreAutre

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

En bref

Citations17
Publié2019
Routes d'admission1
Résumé présentoui

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