Federalism and the management of conflict in multinational societies
Bibliographic record
Abstract
Introduction In a special issue of The Annals of the American Academy of Political and Social Science entitled ‘Ethnic Conflict in the World Today’, Martin Heisler argued that ‘the peaceful and effective management of conflict between ethnic groups involves the building of rules and institutions for coexistence in a single society, state and economy’ (Heisler 1977). The question we address in this chapter is: How effective is federalism as an institutional framework for managing these kinds of conflict? In particular, when, and under what conditions, does federalism constitute a stable, enduring solution, rather than a transitional phase on the way either towards secession or centralization? Is it inevitable that federal solutions are unstable? Are some models of federalism more likely to succeed than others and if so, under what conditions? We will look at one long-standing democratic federal system, Canada, and three newly emerging federal or quasifederal systems, Belgium, Spain and Scotland. All are multinational federations, rather than what Juan Linz calls mononational federations, such as Germany or Australia (Linz 1997b). We will also focus on managing conflict among groups that are territorially concentrated. Federalism itself is not a plausible solution when minorities are spread widely throughout the majority population, although Elkins (1995) has shown that many federalist devices can be used even in these cases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".