Navigating Power Dynamics in Virtual Interviews with Sex Workers during COVID-19: A Researcher-Participant Perspective
Bibliographic record
Abstract
Sex workers may show extreme sensitivity to power relations during qualitative research due to the previous experiences of stigmatization and marginalization. The purpose of this article is to analyze how technologically mediated communication between researchers and participants during an interview may influence the scope of control exercised by the interactional partners. During the first wave of the COVID-19 pandemic, I conducted 16 qualitative phone and videoconference interviews with female sex workers in Poland discussing the social stigmas they encounter. Each interview was followed up with extensive field notes that were analyzed using the procedures of grounded theory methodology. These very field notes serve as the basis for the paper herein. As a result of the analysis, I distinguished areas of power negotiated by the interviewer and interviewees in successive phases: before, during, and after the interview. The sense of control over the respective aspects of a study may contribute to the establishment of a more democratic power relationship between the researcher and the participants who belong to a population bearing a stigma.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".