Research 101: A process for developing local guidelines for ethical research in heavily researched communities
Bibliographic record
Abstract
BACKGROUND: Marginalized communities often attract more than their share of research. Too often, this research benefits researchers disproportionately and leaves such communities feeling exploited, misrepresented, and exhausted. The Downtown Eastside (DTES) neighborhood of Vancouver, Canada, has been the site of multiple public health epidemics related to injection drug use as well as the site of much community-led resistance and struggle that has led to the development of cutting-edge harm reduction interventions (e.g., North America's first supervised injection facility, Insite) and a strong sense of community organization. This background has made the DTES one of the most heavily researched communities in the world. Amidst ongoing experiences of unethical or disrespectful research engagement in the neighborhood, a collaboration between local academic researchers and community representatives developed to explore how we could work together to encourage more respectful, community-responsive research and discourage exploitative or disrespectful research. METHODS: We developed a series of six weekly workshops called "Research 101." These workshops brought together approximately 13 representatives from peer-based organizations in the DTES with a variety of experiences with research. Research 101 created space for community members themselves to discuss the pitfalls and potential of research in their neighborhood and to express community expectations for more ethical and respectful research. RESULTS: We summarized workshop discussions in a co-authored "Manifesto for Ethical Research in the Downtown Eastside." This document serves as a resource to empower community organizations to develop more equitable partnerships with researchers and help researchers ground their work in the principles of locally developed "community ethics." Manifesto guidelines include increased researcher transparency, community-based ethical review of projects, empowering peer researchers in meaningful roles within a research project, and taking seriously the need for reciprocity in the research exchange. CONCLUSIONS: Research 101 was a process for eliciting and presenting a local vision of "community ethics" in a heavily researched neighborhood to guide researchers and empower community organizations. Our ongoing work involves building consensus for these guidelines within the community and communicating these expectations to researchers and ethics offices at local universities. We also describe how our Research 101 process could be replicated in other heavily researched communities.
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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 | 0.600 | 0.504 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.012 | 0.035 |
| Research integrity | 0.023 | 0.052 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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".