Research Experience and Agreement with Selected Ethics Principles from Canada's "Tri-Council Policy Statement--Ethical Conduct for Research Involving Humans"
Bibliographic record
Abstract
An online survey was conducted of students, instructors, and researchers in distance education regarding principles for the ethical treatment of human research subjects. The study used an online questionnaire, based on principles drawn from Canada’s Tri-Council Policy Statement, Ethical Conduct for Research Involving Humans (TCPS, 2003), which the authors had sometimes found problematic in their own distance education practice (as researchers, and in their work with graduate students). Overall, findings showed that respondents tended to agree with the principles presented, whether consistent or not with the TCPS; however, those with more research experience showed a tendency to agree more with questionnaire items that were consistent with the TCPS, and less with those items not consistent with the Policy, a pattern that was more pronounced in a group of twenty-five published researchers. Conclusions were that research experience was associated with greater agreement with the Policy’s principles, with ethics issues, and with REB experience; that, by their own admission, many participants were not well acquainted with the TCPS; and that efforts to address the reservations of distance education researchers about ethics review should include the involvement of experienced researchers, as this group likely best represents the ethical norms and practices of the field.
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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 | 0.040 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".