Bibliographic record
Abstract
Does the moral requirement that medical research comparing the effectiveness of two treatment methods be done only when there is community level equipoise also apply to research in teaching and learning comparing the effectiveness of two instructional methods? This article argues that it does. It evaluates three claims that the requirement does not apply to research in teaching and learning. One is the idea that the equipoise standard mixes up the ethical rules for practice with those for research. So it applies neither to research in medicine nor research in teaching and learning. The second is the idea that research in teaching and learning is different than research in medicine. The ethical basis for the equipoise requirement in medical research does not exist for research in education and so does not apply. Finally, the point is sometimes made that satisfying the equipoise requirement can be outweighed or more than compensated for by other factors when evaluating the ethics of research. For example, the knowledge gained about the comparative merits of different methods of teaching and learning might be so significant that it offsets any moral demand for equipoise or uncertainty.
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 | 0.414 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.156 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".