Working With Research Ethics: The Role of Advisory Committees in\n Community-Based Research
Bibliographic record
Abstract
Research confirms that the university ethics review process, the only formal ethics review process that most community-based researchers in Nova Scotia have experienced, is neither appropriate nor desirable for most community-based researchers.Instead, most community-based researchers conduct their own informal ethics review using an Advisory Committee.These committees facilitate and implement ethical considerations as they emerge out of the specific dynamics as the research progresses.This paper outlines how Advisory Committees are set up, how they attempt to include in decision-making as many voices as possible, especially those of the research participants, and how they monitor what researchers take from and give back to communities, including making sure that research participants' words are returned to them in an accessible form.RESUME La recherche confirme que le processus de l'examen de I'dthique mene par les universitds, le seul processus formel de l'examen de l'ethique que la plupart des recherchistes de la Nouvelle-Ecosse connaissent, n'est ni approprie ni desirable pour la majorite des recherchistes qui travaillent dans la communaute.Au contraire, la plupart des recherchistes qui travaillent dans la communaute menent de facon informelle leur propre examen de I'dthique en se servant d'un comite consultatif.Ces comitis facilitent et implantent les considdrations dthiques au fur et a mesure qu'elles surgissent des dynamiques particulieres au cours de la recherche.Cet article expose les grandes lignes de la facon dont on met sur pied les comites consultatifs, et comment ils essaient d'inclure dans la prise de decisions autant de voix que possible, surtout celles des participants a la recherche et comment ils surveillent ce que les recherchistes retirent des communautes et ce qu'ils leur redonnent, y compris s'assurer que ce que les participants a la recherche ont dit, leur soit retourne dans un format qui leur soit accessible.
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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 | 0.562 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.036 | 0.035 |
| Scholarly communication | 0.041 | 0.020 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".