Who Am I Today? Roles Occupied While Conducting Feminist Informed Interviews With Women Who Use Drugs
Bibliographic record
Abstract
There have been increased calls for researchers to be more attentive to how their ‘roles’ can influence participants and data generation processes. This remains uniquely true for those conducting research with women who use drugs and are housing insecure who experience diverse vulnerabilities. Despite these calls, there remains limited guidance for early career researchers who intend to engage these groups in research, furthering harm. Drawing on a reflexive analysis of a series of feminist-informed interviews conducted with women who use drugs and experience housing insecurity living in Ontario, Canada, I illustrate how diverse not attributed to me by participants shaped data generation. The roles I believe were attributed to me through the interviews include being a “ Service Provider, ” “Sensitive Listener,” “Relatable Person,” and a “Good Kid .” I argue that early career researchers must leverage practices such as reflexivity to consider how elements of one’s positionality influence researcher’s ability to achieve the goals of feminist-informed interviewing. This paper ends with guidance for early career researchers on how to create safe and inclusive research practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.039 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".