Fixing What Ain't Broke: The New Norm of Fixed Date Elections in Canada
Bibliographic record
Abstract
Since 2001, legislation implementing fixed dates for general elections has been passed by the federal government, and most provincial and territorial governments. The notion that general election dates are now fixed, however, is flawed. In my submission to Changing Political Landscapes, I will explore the fledgling norm of fixed date elections in Canada and examine the aspects of the legislation which call into doubt the fixedness of these elections. With a review of the literature on the subject, I begin by inquiring into the emergence of this foreign phenomenon into Canadian electoral politics and the justification for its extensive adoption. Comparing the legislation across jurisdictions, I analyze the basic construct of fixed date election legislation in Canada, survey similarities and differences, and discover how fixed dates for elections are ultimately avoidable. As a result, I find that election dates are not truly fixed in Canadian jurisdictions where fixed date election legislation has been enacted.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".