Strategic science in the public interest : Canada's government laboratories and science-based agencies
Bibliographic record
Abstract
AcknowledgmentsAbbreviationsIntroductionPart One: Historical Context and Analytical Framework Government S&T Labs and Agencies as Institutions: Towards Middle-Level Approaches Analytical ApproachPart Two: Case Studies of R&D-Focused Labs and RSA-Focused Agencies The CANMET Mining and Mineral Sciences Laboratories and Canada's Transformed Mining Sector The CANMET Energy Technology Centre--Devon and the Alberta Oil Sands The Environmental Technology Centre and Environmental Protection The National Wildlife Research Centre and Frontline Sustainable Development Related Science Activities in the Regulatory and Monitoring Process ConclusionsAppendix: Canadian and Comparative Science and Technology DataReferencesIndex
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".