Research and confidentiality: Legal issues and risk management strategies.
Bibliographic record
Abstract
Maintaining the confidentiality of research subjects and research data is essential to the research process. However, the legal landscape surrounding the concept of confidentiality is a dynamic one. This article discusses why confidentiality in research is important, various threats to it, efforts to protect it through a privilege, as well as other statutory and regulatory provisions relevant to research confidentiality. The article concludes with a discussion of risk management tools that researchers concerned with maintaining confidentiality can use in the design and implementation of research. Ogden has continued his research on assisted and is now studying the consensual deathing industry. Part of this research involves scrutiny of social reactions to those involved in consensual death, as occurs when persons are charged with crimes such as counselling suicide or aiding and abetting a suicide, a violation of section 241(b) of the Criminal Code of Canada. When such charges were laid against a Vancouver Island woman, Ogden attended the preliminary hearing early in 2003 to observe and take notes. Outside the courtroom the Crown prosecutor informed Ogden that he was a person of interest in the case because of his presumed research-related knowledge, and subsequently subpoenaed him, apparently in the vague hope that he might have information that could aid the prosecution. 1 Russel Ogden is a Canadian researcher whose work has been subpoenaed several times by prosecutors. He has resisted the subpoenas, which have eventu- ally been withdrawn, and the matter has caused considerable commentary in Canada about whether researchers should have a privilege against being forced to disclose their notes and related materials in legal proceedings. 2 Ogden's case, and others like it 3 raise important issues regarding the confi- dentiality of research data. Researchers have long assumed that maintaining
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".