1. Anticipating Law: Research Methods, Ethics, and the Law of Privilege
Bibliographic record
Abstract
Our ethical obligation to protect the research confidentiality of individual participants is challenged when third parties use subpoenas in the context of criminal proceedings and civil litigation in an effort to order the production of confidential information. This paper discusses strategies researchers may employ in order to maximize their legal ability to maintain confidentiality in spite of those challenges. Use of existing statutory protections is the first choice, but these are available for only a subset of research related to health and criminal justice issues. In situations where statutory protections are not available, the Wigmore criteria may act as a guide for the design of research that maximizes researchers' ability to protect research participants by advancing a case-by-case claim for researcher-participant privilege. We discuss the legal basis for this conclusion and outline procedures that may be used to further strengthen confidentiality protections.
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 | 0.275 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.103 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".