Broadening Core Research Ethics Principles: Insights from Research Conducted with Black Communities
Bibliographic record
Abstract
Drawing from a 2023 symposium panel that focused on conducting health equity research with Black communities, we propose to expand our interpretation of core research ethics principles. In light of a surge of research conducted in Black diasporic communities since the 2020 killing of George Floyd, the symposium sought to enhance the quality and impact of research involving Black Canadians. We contend that by broadening the interpretation and application of respect for persons, beneficence, and justice, researchers will conduct impactful and transformative research projects that foster health equity. We emphasize the importance of not limiting the core principle of respect for persons to individual participants but to extend it to communities throughout the research process. Furthermore, we suggest that researchers can deepen their commitment to the core principle of beneficence or concern for welfare and design relevant and empowering research projects through meaningful community involvement. We highlight that to further the implementation of the core principle of justice, scholars should adopt a human development approach and mobilize innovative outreach recruitment strategies to ensure that Black communities have the opportunity to participate in biomedical and public health research while also benefiting from the knowledge produced.
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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 | 0.246 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.048 | 0.105 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".