Two Cheers for a Cabinet Manual (And a Note of Caution)
Bibliographic record
Abstract
This article discusses the advantages and disadvantages of two approaches to the descriptive codification of Canada’s constitutional conventions in a Cabinet Manual. The proponents of a Manual point to its utility, while its detractors highlight the dangers inherent in the executive’s role in the Manual’s production and amendment process. It evaluates the likelihood of these benefits and hazards by assessing recent scholarship assessing the Cabinet Manuals of New Zealand and the United Kingdom. A review of the historical and political circumstances of the introduction and development of the Cabinet Manuals of New Zealand and the United Kingdom reveals that their differences have led to disparate results. The author argues that the most important lesson for Canadian proponents to learn from these examples is the importance of a transparent amendment process. Should Canada adopt a Cabinet Manual — a development that would produce tangible benefits — those setting out the processes governing its adoption and amendment should pay close attention to New Zealand’s. To do otherwise would risk allowing the executive to produce ‘soft law’ that inhibits the development of conventions that regulate the royal prerogative, as has been the case in the United Kingdom. The article demonstrates that this would be a particularly unwelcome development within a constitutional system in which the recognition of constitutional conventions is justiciable.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".