Bibliographic record
Abstract
This dissertation, structured as a collection of three articles, aims to answer the following question: which\n\t\t\t\t kind of right to secede from a modern democratic state, if any, can be acknowledged from a democratic\n\t\t\t\t republican point of view? The first article shows that secession conflicts pose threats for democratic\n\t\t\t\t republican principles (particularly, freedom and inclusion), which no current theory of right of secession\n\t\t\t\t (TRS) overcomes. The second article finds that a constitutional right of secession, like Quebec's one, could\n\t\t\t\t minimize those threats because of its non-unilateral nature, but that its limitation to constitutional law\n\t\t\t\t undermines this potentiality. Finally, the third article develops a democratic republican TRS based on a\n\t\t\t\t multilateral right of secession for any democratic secessionist community, coupled with a multilateral right to\n\t\t\t\t unity for its democratic host state. The international democratic community would arbitrate and monitor any\n\t\t\t\t secession conflict according to this framework.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".