Amendments to the Civil Code of Québec's Research Provisions: A Legislative Comment
Bibliographic record
Abstract
On 14 June 2013, Quebec’s National Assembly passed Bill 30, an Act to amend the Civil Code and other legislative provisions with respect to research, which entered into force the same day. Bill 30 amended research provisions in the Civil Code of Quebec (CCQ) pertaining to research, specifically articles 20-22, 24, and 25, as well as a section of the Act respecting health services and social services, modifying a complaint mechanism for research participants and their heirs or legal representatives. The goal of Bill 30 was to eliminate confusion surrounding the provisions and remove a number of barriers to research activities in Quebec – particularly where the research presented minimal risk to participants – so that the scientific community could investigate important research questions. The CCQ amendments are welcome in many respects, foremost because they bring much-needed revision to an anachronistic section of the Code that reflected a twentieth-century research environment. Replacing the term “experiment” with “research,” for example, is to be applauded. The amendments also warrant criticism, however, and in this legislative comment we critically discuss both the improvements and missed opportunities that Bill 30 presents, particularly in the con- text of biomedical research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.071 | 0.041 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".