Bibliographic record
Abstract
2.1 Dictator Game Behaviours 26 5.1 Voter Turnout in Canada 84 5.2a-h Difference between Partisan Identification and Voting Intentions of Voters and Non-Voters in Seven Elections 86 5.3 Official and Canadian Election Survey Turnout 101 6.1 Economic and Fiscal Weight of U.S. States, Canadian Provinces, and French Regions versus Decentralization of Local Upper-Level Expenditures 110 7.1a Vote Intentions over the Campaign, Ontario 2003 (3-Day Averages) 136 7.1b Vote Intentions over the Campaign, Quebec 2011 (3-Day Averages) 137 7.2 The Media Issue Agenda over the Campaign (3-Day Averages) 140 7.3 Campaign Effects by Group, Ontario 2003 149 7.4 Campaign Effects by Group, Quebec 2011 152 9.1 Differentiation of the Electoral Supply 209 9.2a Democracy Satisfaction 214 9.2b Preference for Democracy 215 9.3a Electoral Supply and Democracy Satisfaction among Electoral Winners and Losers 224 9.3b Electoral Supply and Democracy Satisfaction among Policy Winners and Losers 225 9.4a Electoral Supply and Preference for Democracy among Electoral Winners and Losers 226 9.4b Electoral Supply and Preference for Democracy among Policy Winners and Losers 227 10.1 Sequence of Psychological and Mechanical Effects 240 10.2 Distribution of Absolute and Relative Electoral System Effects 247
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 | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.801 | 0.594 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".