Muzzles and Mixed Messages: Issues Between Science and the Federal Government In Canadaâs Past and Present
Bibliographic record
Abstract
This paper will examine the historical relationship between the federal government of Canada and the scientists it has employed over the past few decades. It will compare science policy and practices from leaders such as Diefenbaker and Trudeau to the policies currently followed by Stephen Harper's government. It will then ask what might be achieved by following those policies, despite the criticism received by the science community. The paper will ultimately argue that the federal government and scientists have often had a contentious relationship, and the policies the Conservative government is currently implementing are not new. It will also argue that the these Conservative policies are intended to present a unified political front to the broader Canadian public and to the world.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.068 | 0.041 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".