Minority Nations in the Age of Uncertainty: New Paths to National Emancipation and Empowerment
Bibliographic record
Abstract
For thirty years, Alain-G. Gagnon has been one of the world’s leading experts on federalism and multinational democracies. In Minority Nations in the Age of Uncertainty, he presents an articulate and accessible introduction to the ways in which minority nations have begun to empower themselves in a global environment that is increasingly hostile to national minorities.Comparing conditions in Quebec, Catalonia, and Scotland, Gagnon offers six interrelated essays on national minorities, processes of accommodation, and autonomy and self-determination within a modern democratic context. Based on a long career of scholarly study and public engagement, he argues that self-determination for these "nations without states" is best achieved through intercultural engagement and negotiation within the federal system, rather than through independence movements.Already translated into fifteen languages from the original French, Minority Nations in the Age of Uncertainty is an essential text on the theory of multinational federalism and the politics of minority nations.This edition also features a foreword by noted political scientist and philosopher James Tully that discusses the significance of Gagnon's work
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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 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".