Bibliographic record
Abstract
Tables and Figures Tables 10.1 The core cultural industries in Canada 207 13.1 Canada's largest cities 267 13.2 Municipal expenditure responsibilities, provinces and territories, 2008 272 13.3 Municipal tax measures across Canadian provinces 275 13.4 Municipal expenditures, provinces and territories, 2008 and 2013 277 13.5 Distribution of municipal expenditures and revenues, provinces and territories, 2013 278 13.6 Intergovernmental grants as a percentage of municipal expenditures, 2008 and 2013 281 13.7 Distribution of municipal expenditures and revenues, Toronto, 2008 and 2014 283 13.8 Distribution of municipal expenditures and revenues, Montreal, 2008 and 2014 285 13.9Canada's municipal infrastructure deficit 287 13.10 Distribution of municipal expenditures and revenues, Vancouver, 2008 and 2014 290 14.1 Protest activity in Montreal and Toronto in 2015 311
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 | 0.002 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.716 | 0.354 |
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".