Memorandum on Intellectual Property Issues
Bibliographic record
Abstract
This memorandum is structured around some of the questions that may arise within the context of the Action for Health project.It should be noted that this memorandum deals with legal issues at the level of legal principles and, as in many other fields, it is impossible to predict with any degree of certainty how principles will be applied in any particular situation.Many of the issues discussed below have not yet been litigated in Canada.Nothing contained herein is intended to constitute legal advice and anyone who has specific questions should consult with a lawyer.ISSUES 1.What intellectual property issues could arise with health information websites and the maintenance of health databases?a.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.052 | 0.055 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".