Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".