Fixed versus Flexible Election Cycles: Explaining innovation in the timing of Canada’s Election Cycle
Bibliographic record
Abstract
This paper argues that there is an efficiency gain underlying the recent adoption of legislation calling for a fixed four-year governing term by the federal and most provincial governments in Canada. The efficiency gain arises from foreclosing an externality produced by the constitutional provision that sets a maximum length for a legislative term (five years) while allowing the governing party (through the Governor General) to dissolve the House early. Because the opportunistic use of surprise can improve the governing party’s probability of winning, strategic choice can lead to elections being held at times that most disadvantage the incumbent’s rivals. Evidence from Canada is introduced suggesting that federal elections became less predictable through successive reductions in the campaign time given to competitors, thus raising the cost of this externality. The same reasoning suggests that the party most likely to propose this legislative innovation will be the party in opposition rather than in power and/or the new leader of an established party facing loss in the upcoming election. By fulfilling the fixed term even when it could benefit by calling the election early, the party establishes a precedent that raises the political cost to others of cancelling the fixed term legislation.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".