Early Contributions to the Evolution of the Canadian Scientific Integrity System: Institutional and Governmental Interaction in the Policy Diffusion Process
Bibliographic record
Abstract
Academic institutions and research funders have in the last decade devoted considerable effort to developing policies to support academic integrity and prevent misconduct. In this study, we consider the extent to which various initiatives of Canadian federal and provincial (Québec) funders have affected the development of institutional research integrity/misconduct (RIM) policies. Examining the creation and modification dates of 32 institutional RIM policies, we find that federal but not provincial initiatives appear to have the greatest impact on the development of RIM policies. Idiosyncrasies in the creation dates, as well as lack of evidence of a systematic pattern in modification dates, suggest a complex system that is often insulated from certain government initiatives. These results lead us to conclude that there should be greater consistency in the development or updating of RIM policies to ensure the appropriate treatment of misconduct and to encourage behaviour that meets the highest standards of research integrity.
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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 | 0.095 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.033 | 0.035 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".