Bibliographic record
Abstract
THE WRITING OF HISTORIES of Canadian federal elections has increased in recent years, and for good reason.Elections tell us a lot about the country and its peoples, about the issues that were considered important, about the causes that motivated people into action, and about the things that united Canadians, as well as those that divided them.In addition, more can be learned from an analysis of the various other aspects of an election campaign, from the number of parties, their platforms, and their candidates, to the size of the turnout, who could vote and who could not, and the regional distribution of the vote.Federal elections provide the closest thing to a snapshot of the country at any given moment.In a simpler way, elections are also memorable and remarkable moments in history, with people of diverse backgrounds and prominence debating important matters of public interest.Hundreds of thousands participate, some just by voting, others more actively, in the press; on the radio, television, and Internet; on the campaign trail -criticizing opponents and cajoling voters, assembling and managing teams of friends and allies, participating in rallies and other public events, and raising campaign funds.All these groups are brought together in pursuit of the same goal -victory at the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.076 |
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".