“Trying to square the circle”: Research ethics and Canadian higher education
Bibliographic record
Abstract
At the European Conference on Educational Research (ECER) in 2016, a panel presented the findings from a survey initiated by the European Educational Research Association Council to examine educational researchers’ experiences with the research ethics review process at their universities. Some researchers appeared to be looking to North America for models to govern and regulate university research ethics. In response, our inquiry began from the question: what can European researchers learn from the way ethical review structures and processes have developed in Canada? But as we approached this question, we encountered a more immediate question: to what extent is it possible to address a diversity of research–ethical concerns via a single, bureaucratic policy? Then, how do standardized ethics regimes fail to account for non-standard research—and thereby fail researchers, participants, and communities?; and what is the alternative? In this paper, we explore the history of the development of an ethics regime for Canadian universities, and changes over time. Based on this review, as well as our personal experiences with community-based research, we argue that efforts to regulate the diversity of social sciences research via a uniform policy almost inevitably miss the mark: one ends up trying to “square the circle”.
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 | 0.144 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.052 | 0.101 |
| Scholarly communication | 0.031 | 0.014 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".