Lessons learned from the <i>Quebec Secession Reference</i> before the Supreme Court of Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present chapter deals with the practice of secession in North America. It is both a bloody story as well as one of virtue. The contrast between the approach adopted by courts in the United States following the attempt by the southern states to secede in 1861, and that adopted by the Supreme Court of Canada in 1998, could not be more striking. Both attempts at secession (one real and the other hypothetical) were firmly opposed by the federal government of the United States. However, one secessionist bid was crushed by war, while the other was discussed in a court of law and became the object of legislative acts. Even when the U.S. Supreme Court discussed the issue of the legality of secession in the famous Texas v. White Case, it took the view that:
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it