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Morality Policy

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

Bibliographic record

VenueOxford Research Encyclopedia of Politics · 2019
Typereference-entry
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMoralityLegalizationPornographyPoliticsSociologyValue (mathematics)Political scienceLaw

Abstract

fetched live from OpenAlex

Abstract 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.078
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.015
Scholarly communication0.0190.013
Open science0.0030.008
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.0780.025

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.457
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2019
Admission routes1
Has abstractyes

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