The Role of Educational Developer in Supporting Research Ethics in SoTL
Bibliographic record
Abstract
This chapter stems from popular misconceptions demonstrated by educators who lack familiarity with the significance and necessity of honoring ethical guidelines and practices when conducting SoTL research. The authors articulate the value of incorporating ethical principles and practices in research design and provide educational developers with much needed critical information about ethical considerations when conducting SoTL research. An overview of the purpose and functions of review ethics boards is included, along with common scenarios involving ethical dilemmas educators may encounter when embarking on a SoTL research study. Reflective questions to contemplate and strategies about how ethical practices can and should be embedded into SoTL research planning and design are explained. A framework and applicable resource are provided so that educational developers may guide and support instructor/researchers through safe and ethical SoTL inquires.
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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 | 0.050 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".