Ethical and Legal Strategies for Protecting Confidential Research Information
Bibliographic record
Abstract
Abstract The paper begins with an outline of some legal and ethical principles regarding research confidentiality that frame researchers' choices, and then reviews the common law on privilege in Canada and the U.S. to show how researchers can design their research to maximise the legal protection of confidential research information. The paper describes various disciplinary ethics codes and the new federal Tri-Council Policy Statement on ethics to illustrate the principles that should be considered in the unlikely event that a Canadian court orders disclosure of confidential information that could harm a research participant. We conclude by proposing that universities and the three granting councils should campaign for statutory protection of research participants along the lines of the confidentiality certificates that are currently available in the United States for research on sensitive topics such as drug use, criminal activities, sexual behaviour, and genetic information.
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 | 0.379 | 0.372 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.097 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.029 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".