Bridging the Gap between the Research Ethics Board and the Scholarship of Teaching and Learning
Bibliographic record
Abstract
In 2016, Dalhousie University’s Research Ethics Board created an interdisciplinary working group to identify the key ethical challenges of SoTL research, with the overarching aim of recommending best practices and communicating these to researchers in order to support and expand the conduct of ethically sound SoTL research. This essay reflects on the lessons learned through this process and shines a light on the three most contentious arenas that emerged: using class time to conduct SoTL research, integrating Students Ratings of Instruction (SRI) into SoTL, and incorporating student work as a data source. This essay contributes to the emerging conversation around ethical SoTL research in two important ways. First, we argue for more lenient REB protocols that encourage SoTL research by exposing how restrictive interpretations of key issues serve as obstacles for student-centered research. Second, we introduce new tools designed to address these impediments, including the first-ever interactive user guide. The overarching aims of this essay are (a) to help SoTL researchers navigate this complex terrain, and (b) to encourage other Canadian REBs to consider implementing more permissive regimes.
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 | 0.382 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.054 | 0.155 |
| Scholarly communication | 0.063 | 0.029 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.025 | 0.062 |
| Insufficient payload (model declined to judge) | 0.004 | 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".