Bibliographic record
Abstract
Diversity is one of the most contested issues in domestic and international politics. Debates about ethnic, national, linguistic, religious, and economic diversity and its accommodation in viable and legitimate polities feature prominently in discussions among academics and practitioners of comparative politics, conflict-resolution studies, political sociology, and political theory. There are several types of “old” and “new diversities and their potential to create conflict have been frequently addressed through federal arrangements. First, there is diversity pertaining to cultural, ideological, racial, religious, and linguistic predispositions. When these are concentrated territorially, they may be more difficult to manage institutionally, and they are the ones for which federal arrangements are deemed the most appropriate. Second, the existence of politically mobilized territorial or national self-defined identities in multi-ethnic or multinational societies represents a paramount challenge for the governance and accommodation of differences. Third, there is diversity concerning socio-economic resources and the interests of groups concentrated territorially. Socio-economic differences revolve around the allocation of socially valued goods and the redistribution of resources among territories, and are sometimes a common rationale of federal arrangements. \nThis paper explores how institutions and ideas have helped accommodate ethno-linguistic or religious diversities, empower ethnic or linguistic minorities, manage conflicts, and establish a legitimate, stable, and cohesive order in twelve federal countries: Australia, Belgium, Brazil, Canada, Ethiopia, Germany, India, Nigeria, Russia, Spain, Switzerland, and the United States of America.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".