Bibliographic record
Abstract
This is an original dataset of micro-scale election returns for Canadian Federal elections from 2000 to 2021 compiled by Benjamin Forest and Chris Yurris at McGill University. Elections Canada provides detailed election returns but tabulates different types of votes at three different scales. Most votes cast in person on the day of the election are assigned to a specific, geographically defined polling division. Votes cast in advance of the election are assigned to larger geographic units, Advanced Polling Areas, and votes cast in other ways are assigned only to constituencies (ridings) as a whole. We address this issue by assigning advanced and special votes to polling divisions using the spatial pattern of same-day votes. The resulting data has all votes allocated to the micro-scale of the precinct (polling division). The large number polling divisions in Canada (over 50,000 per election) permit analyses from the micro-scale of the precinct (typically 300-500 electors) to the level of constituencies (often more than 100,000 electors). Please contact Benjamin Forest (benjamin.forest@mcgill.ca) for documentation and codebook
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".