Canadian Science Policy and the Public Research Organisations in the 20th Century
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Across OECD nations, public/private partnerships have recently become popular mechanisms in advancing science and technology policies. But Canada has a long tradition of such partnerships. The federal government was involved in the promotion of relations between public research organisations (PROs) and the private sector as early as the start of the twentieth century. In this paper, we trace the evolution of policies promoting the economic utility of public science in Canada. We then present the National Research Council’s Industrial Research Assistance Program (NRCIRAP) and Industry Canada’s Networks of Centres of Excellence (NCE) program as contrasting cases of federal steering. By developing an understanding of these flagship instruments, we seek insight into the wider implications of state intervention in relations between PROs and Canadian industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.020 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it