Statute-Based Protections for Research Participant Confidentiality: Implications of the US Experience for Canada
Bibliographic record
Abstract
Abstract Many types of vital research require protection of communication and information provided confidentially by research participants. In Canada, apart from information collected under the Statistics Act , the only option is a common law balancing test that creates uncertainty insofar as law is made after the fact. This paper explores the option of statute-based protection from the outset. It examines two such protections that have been in place in the United States for decades—revealing their strengths and weaknesses and how they may be applied in the Canadian context.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this metaresearch. It is in the settled core of the field.
Comparative legal analysis of statute-based protections for research participant confidentiality, drawing lessons from US instruments for the Canadian context; the object is how research is governed and regulated in Canada, which is research policy analysis (a reasonable coder could call this core T1).
The paper analyzes legal protections governing research participants and their application to the Canadian research context.
Analyzes statute-based legal protections for research participant confidentiality, comparing US models for Canadian research governance.
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 | 0.076 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.046 | 0.019 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".