Bibliographic record
Abstract
I O N S are the lifeblood of modern democracies.And nowhere has this been more apparent than in Canada, a country cobbled together by bargaining politicians who then continually remade it over a century and a half by their electoral ambitions, victories, and losses.In a continually changing country, the political parties that emerged to manage this electoral competition also found themselves continually changing as they sought to refect and shape the country they sought to govern.Te stories of these politicians, these parties, and these elections are a critical part of the twists and turns that have produced Canada.Canadians have now gone to the polls in forty-four national general elections.Te rules, participants, personalities, and issues have varied over time, but the central quest has always been the samethe chance to win the right to govern a complex and dynamic country.About twice as ofen as not, the electorate has stuck with who they know and returned incumbents to the governing mantle.Only about a third of the time have the government's opponents, promising something new or diferent, been elevated to power.But whatever the outcome over all forty-four elections, the contest has
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.317 | 0.163 |
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".