Deliberations of an Ethically Uneasy Student and Research Assistant in Vancouver’s Downtown Eastside
Bibliographic record
Abstract
How do you begin to research in one of the most researched places in the world? How do you ensure that your research benefits the community you’re studying—treats them like the resilient community they are, instead of reducing them to test subjects for your own professional gain? How do you ensure that you, as a researcher, do not exaggerate the various negative effects academics have previously exposed (but, not addressed) within these marginalised communities? Over the course of 6 weeks, my colleague and I observed a research project in Vancouver’s Downtown Eastside. During that time, we also participated in class at the University of British Columbia’s (UBC) Learning Exchange in the DTES where we studied ethnographic research techniques as well as their effectiveness in researching marginalised communities in the morning, as well as assisted and observed a community-based researcher in the afternoon. This ethnographic account of my time at the UBC Learning Exchange analyses the role deliberate intention can play in ensuring ethical standards of conducting research in Vancouver’s Downtown Eastside, especially given the significant number of marginalized people residing in this vibrant community.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.013 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".