Bibliographic record
Abstract
2.1 Media coverage of Calgary mayoral candidates 36 2.2 Issue importance in the 2017 Calgary election 39 2.3 Vote share for Nenshi, by ward 41 2.4 Vote choice for Nenshi -marginal effects 42 2.5 Partisan attribution, by candidate and respondent partisanship 47 2.6 Nenshi vote, by respondent partisanship 48 2.7 Predicted probability of vote, by ideological distance and copartisanship 49 3.1 Issue importance in the 2017 Montreal election 59 3.2 Vote share for Plante, by district 61 3.3 Vote choice for Plante -marginal effects 65 3.4 Citizens' perceptions of the 2017 Montreal mayoral race 69 3.5 Relationship between Plante vote and relative odds of Plante victory 71 4.1 Issue importance in the 2017 Quebec City election 85 4.2 Vote share for Labeaume, by ward 87 4.3 Vote choice for Labeaume -marginal effects 89 4.4 Correlates of mayoral vote choice (with mayoral evaluation) 91 4.5 Perceptions of likelihood of Guérette victory and support for Gosselin 94 5.1 Vote intention for the top five candidates, and 90% confidence intervals 105 5.2 Issue importance in the 2018 Vancouver election 109 5.3 Vote choice for Stewart -marginal effects 113 5.4 Treatment effects of group-norm prime on willingness to include a statement, by expected approval of one's ideological identity group for opposing lowering rental and housing prices 120
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.688 | 0.442 |
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".