Barriers to Equity, Diversity and Inclusion in Canadian Research Ethics Board membership: Challenges and opportunities for reform
Bibliographic record
Abstract
This article reflects upon the current state of Equity, Diversity and Inclusion (EDI) on Research Ethics Board (REB) membership in Canada. As post-secondary education institutions strive to increase EDI initiatives across all areas, diversity among the REB membership becomes increasingly critical. Increasing EDI on the REB is complex in the context of colonialism and a discipline that has historically mistreated those from equity-deserving groups. Many barriers to achieving diversity in academia exist that are also reflected in the REB membership. REBs lacking in diversity may struggle to conduct robust ethical reviews, and without full institutional support, increasing diversity in the membership remain a challenge. Diversity amongst community members adds another layer to this complexity with additional barriers such as lack of inclusive recruitment strategies and equitable compensation. Despite community members being central the mandate of the REB, they can be perceived as secondary to affiliated subject matter expert members. This perception de-values the work of the non-affiliated community member, creating conditions of tokenism and power imbalances. Given the unique standing of the REB within the research enterprise, it is well positioned to be a leader in the EDI space. Barriers identified are surmountable and with genuine effort, the REB can champion EDI. It will take full institutional support to enact change and disrupt barriers to EDI for the REB to reach an ideal state of authentic EDI in its membership and processes. Such endeavors can only act to strengthen the ethics review of research and increase research excellence throughout Canada.
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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 | 0.234 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.060 | 0.058 |
| Scholarly communication | 0.046 | 0.020 |
| Open science | 0.010 | 0.034 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".