Bibliographic record
Abstract
This chapter deals broadly with the intersection of science and governance.As a topic this has received considerable attention within the literature on science policy, particularly since the 1960s, although perhaps less so in Canada than elsewhere.1 Academic research centers dedicated to examining the interplay of science and governance began to emerge in other countries in this early period.In Canada, however, the major contributions to advancing understanding of science policy tended to come in the form of government reports and advisory body studies.Scholarly attention to science policy was rare.A key exception, of course, was the early scholarship of Bruce Doern (See Table 1).His 1972 book, Science and Politics in Canada, is a seminal volume that remains of value today.More recently, Bruce renewed his interest and scholarship on issues of science and innovation policy to the benefit of many younger researchers, including this author.Throughout his career, Bruce's leadership has been instrumental in elevating science policy as an explicit area of focus in the study of public policy and in helping to build a small but vital community of science policy scholars and practitioners.The continuing importance of science policy and science advisory mechanisms is highlighted by one analyst who asserts that "the key to understanding modern government lay in the interplay of expert knowledge and politics." 2 Similarly, Jasanoff contends that in their expanding roles as advisors, scientists have emerged as a "fifth branch" of government.3 Other scholars approach the issue from a more balanced perspective.For example, Gillespie writes with awareness that "much of science has in general little or nothing to do with government, and that much of government has little or nothing to Table 1 The rich vein: Bruce Doern's early scholarship on science policy and the scc
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".