A Tale of Two Crises: Developments in Abeyance Theory in Canada and the United States
Bibliographic record
Abstract
This article reviews and develops the theory of “constitutional abeyances” in the context of recent political developments in Canada and the United States. The literature developed in the Anglo-American context holds that constitutions often have areas of quiet disagreement that relevant actors ignore—“hold in abeyance”—to avoid triggering a wider crisis. The authors argue that recent political developments in the United States and Canada have had different impacts on their respective abeyances, a pattern from which we can draw lessons. In the United States, abeyances appear to be breaking down in the face of the norm-busting presidency of Donald Trump, whereas in Canada they appear to have been strengthened amid a trade war and threats of annexation by Washington. The article concludes with some thoughts on what recent events can show us about how abeyances can either collapse or remain deeply buried.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| 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".