Bibliographic record
Abstract
Average Days of House of CommonsDebate per Government Bill 36 2.2 Total Pages Included in Budget Implementation Acts per Year, 1994-2015 40 3.1 Size of the Public Service 47 3.2 Size of the Executive Cadre 47 3.3 Size of the Ministry 48 3.4 Number of Federal Entities 49 3.5 Government Expenditures 52 3.6 Expenditures as a Percentage of GDP 52 8.1 Growth in Foreign Activity and Canadian Exports 139 8.2 Sources of Loss of Canadian Cost Competitiveness 141 8.3 Shares of Total Federal Government Spending 142 8.4 Household Real Estate as Percent of Disposable Income in Canada 143 8.5 Fiscal Policy Stance 144 10.1 Permanent Residents to Canada 183 11.1 Police-reported Crime Rate, Canada, 1962-2013 214 11.2 Homocides and Attempted Murders, Canada, 1962-2013 215 15.1 Canada Not On Track to Meet Emissions Target 258 15.2A Clean and Healthy Environment, Federal Spending 260
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.612 | 0.336 |
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".