An Introduction to Ethical Considerations for Novices to Research in Teaching and Learning in Canada
Bibliographic record
Abstract
Considering Canada's Tri-Council statement on the ethical conduct for research involving human subjects, we discuss some of the ethical challenges of doing research on teaching and learning in which one's own students and teaching act as the context of such scholarly activity. We advocate establishing basic principles based in the complex relationships in teaching and learning, making reference to the such issues as the potential social consequences for students of choosing not to participate in SoTL research. We propose some principles for those new to teaching and learning research to consider as part of their own ethical considerations. En ce qui concerne l'Énoncé de politique des trois Conseils : Éthique de la recherche avec des êtres humains, nous présentons les difficultés déontologiques de la recherche sur l’enseignement et l’apprentissage au cours de laquelle nos propres étudiants et notre enseignement constituent le contexte de cette activité savante. Nous prônons l’établissement de principes fondamentaux basés sur les relations complexes entre l’enseignement et l’apprentissage et faisons référence à des enjeux comme les conséquences sociales potentielles du choix des étudiants de ne pas participer à la recherche sur l’ACEA. Nous proposons des principes que les chercheurs novices pourraient intégrer à leurs propres considérations déontologiques.
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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 | 0.051 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.027 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".