Bibliographic record
Abstract
Abstract. In recent years, there has been increasing popular and academic debate about how ethnic and racial diversity affects democratic politics and social cohesion in industrialized liberal democracies. In this introduction, different interdisciplinary theoretical approaches for understanding the role of diversity for intergroup relations and social cohesion are reviewed and four extensions to the current literature are proposed. These include taking advantage of a comparative framework to understand how generalizable the consequences of diversity are. A comparative country approach also helps to reveal which policies might be able to mitigate any potential negative consequences of diversity. Most importantly, we propose that the research in this area should include other aspects of social cohesion beyond measures of generalized trust, such as solidarity, attitudes about the welfare state and redistributive justice, as well as political and social tolerance. Finally, research on the effects of diversity might gain more insights from taking less of a majority-centric approach to include the effects on various minority groups as well. Résumé. Ces dernières années ont procuré un sol fertile au débat populaire et universitaire autour des effets de la diversité ethnique et raciale sur la politique démocratique et sur la cohésion sociale dans les démocraties libérales industrialisées. Dans cette introduction, nous passons en revue diverses approches théoriques interdisciplinaires permettant de clarifier le rôle de la diversité dans les relations entre les groupes et dans la cohésion sociale et nous proposons quatre ajouts à la littérature courante. Nous suggérons, entre autres, de tirer profit d'un cadre comparatif pour comprendre à quel point les conséquences de la diversité sont généralisables. Une étude comparative des pays aide également à cerner les politiques qui pourraient atténuer les conséquences négatives potentielles de la diversité. Par-dessus tout, nous avançons que la recherche dans ce domaine devrait inclure d'autres aspects de la cohésion sociale à part les mesures de la confiance généralisée, des aspects tels que la solidarité, les attitudes envers l'État-providence et la justice redistributive, ainsi que la tolérance politique et sociale. Finalement, la recherche sur les effets de la diversité pourrait devenir plus instructive en adoptant une approche moins centrée sur la majorité afin d'inclure également les effets sur divers groupes minoritaires.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".