Bibliographic record
Abstract
Drawing from educational research conducted in Canada and Mexico, university researchers explore how culture complicates both the ethics review process and the translation of ethical research principles into practice. University researchers in Canadian contexts seek approval from university Research Ethics Boards to conduct research, following policies outlined in the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans. In this article, the authors consider some cross-cultural ethical dilemmas in relation to educational research, which is often qualitative and interpretive in nature and conducted in schooling settings. Drawing from educational research, the authors conduct both in Canada and in an international context an exploration of how culture complicates both the ethics review process and the translation of ethical research principles into practice. As a result of their experiences, the authors focus specifically on issues related to consent, reciprocity, anonymity and confidentiality, and data representation.
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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 | 0.079 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".