Both Too Much and Too Little: Sources of Federal Instability in Canada
Bibliographic record
Abstract
Federalism is often praised for being able to accommodate diversity within the confines of a single state while preventing secession. Federalism, however, is fraught with tensions and instability. Federalism is typically adopted as a second-best alternative among actors whose first choice is either a more centralized state or a more decentralized state. These preferences persist over time. Instability in federation, then, comes from federal partners pushing in opposite directions at the same time. From this dynamic comes the much-examined propensity for secessionism to develop within federations. Largely unexamined in the literature on federalism, but equally problematic from the standpoint of stability, is the equal and opposite risk of consolidation (or centralization). This article examines sources of federal instability by exploring the origins and evolution of federalism. I use examples from Canada to demonstrate the extent to which my argument is applicable to real world federations. In conducting the exploration, we come to understand how federations can be both unstable and durable.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".