The Ethics and Law of Confidentiality in Criminal Justice Research: A Comparison of Canada and the United States
Bibliographic record
Abstract
The Academy of Criminal Justice Sciences Code of Ethics and the American Society of Criminology Draft Code of Ethics raise the possibility of a conflict arising between research ethics and the law relating to evidentiary and testimonial privilege. However, they say nothing about the form that legal threats to research confidentiality may take in Canada and the United States, the two countries where these codes apply, nor do they describe the strategies that researcher can employ to protect confidential research information in court. The purpose ofthis article is to address these matters. It begins with a brief description ofthe role that confidentiality plays in protecting research participants and maintaining the validity and reliability of criminal justice research. It then describes the legal context in which the researchers' ethical obligations unfold and the strategies that researchers can employ to protect confidential research information when third parties use legal force to try to obtain it. The article argues that the ethical responsibilities of rea s studying criminal justice issues are bet fulfilled and their research participants best protected when researchers ue their understanding of law to design research so as to anticipate the evidentiary requirements of the courts. It concludes with a discussion of the respective advantages and disadvantages of statutory as compared to common law protections for research confidentiality.
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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 | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".