Stressed and oppressed: Insider perspectives on stress induction research with LGBTQA+ People of Color
Bibliographic record
Abstract
There is a substantial body of literature on researcher and participant vulnerability and subjectivity when using qualitative research methods to work with marginalized populations, yet few resources exist for researchers conducting stress induction experiments with these groups - including lesbian, gay, bisexual, transgender, queer, asexual, and other sexual and gender minority (LGBTQA+) people of Color. As a result, I was unprepared for the consequences of conducting the Trier Social Stress Test (TSST) with LGBTQA+ young adults of Color and the vicarious stress that I would encounter as a queer researcher of Color. In this article, I describe the process of conducting the TSST with LGBTQA+ people of Color, reporting participant experiences and feedback and describing my own experiences of inducing stress among members of an oppressed population to which I belong. Using fieldnotes from this study and the extant literature on applications of critical theory and insider research in the qualitative field, I consider possible modifications to socially evaluative stress induction methods for social justice-oriented work with oppressed populations and discuss my perspectives on disrupting positivist assertions in quantitative, psychophysiological research with LGBTQA+ people of Color.
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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 | 0.052 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.049 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 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".