Bibliographic record
Abstract
This book is part of an expanding literature analyzing the role of asymmetry in the territorial politics of contemporary states. Its specific focus is on special status arrangements. These are grants of formally asymmetrical territorial autonomy to territorially concentrated minority communities. Special status arrangements challenge the modern territorial state and citizenship model, which in its purest form implies a standardized distribution of territorial political authority and equal citizenship rights across a state. Despite these tensions, the governments of a number of states have used special status concessions to accommodate the demands of mobilized, or mobilizeable, minority communities. Special status concessions have been made in both federal and unitary states and those of diverse regime types. They have also been made in societies with distinctive histories and a range of cultural, social, political economy, geopolitical, and other characteristics (see Henders 1997; Steiner 1991; Hannum 1990; Lapidoth 1997; see also Heraclides 1992; Heisler 1990; Laponce 1987). Special status arrangements exist for Quebec in Canada and Scotland in the United Kingdom, in Muslim areas of Mindanao in the Philippines, and the Atlantic coast regions of Nicaragua, to name a few examples. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.437 | 0.267 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".