Bibliographic record
Abstract
In many institutions, the institutional review board/research ethics board (IRB/REB) uses the traditional audit approach that emerged from the biomedical community (e.g., Nuremburg Code, Belmont Report) to review the ethical acceptability of research using humans as participants. This approach is guided by participant protection and risk management concerns. This article discusses the approach to ethics review currently being adopted at a large Canadian university in transition from a teaching to a research institution. It articulates the values that guide the REB in its deliberations and explores how these values support a facilitative—rather than an audit—approach to ethics review. Two case studies of innovative qualitative inquiry are discussed to demonstrate how Concordia University (Montreal, Canada) has adopted an approach to ethics review that attempts to encourage, engage, and support qualitative researchers in their research initiatives, while respecting established legal and ethical guidelines targeted primarily at clinical research.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.434 | 0.479 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".