The Emotional and Psychological Labor of Insider Qualitative Research Among Systemically Marginalized Groups: Revisiting the Uses of Reflexivity
Bibliographic record
Abstract
In response to decades-long exclusionary practices, academic institutions are now recruiting early career researchers (ECRs) from systemically marginalized populations who specialize in equity-related research. As a result, these ECRs are likely to conduct research within their communities on topics that have personal relevance-insider research. Methodological training for insider research places an emphasis on methods, such as reflexivity, to ensure rigor; however, the emotional and psychological impacts of these research methods on the researcher are seldom discussed. Therefore, I use analytic autoethnography to illustrate the embodied impacts of conducting insider research using an example of personal relevance and argue that methodological practices require an embodied reflexivity that centers the researcher and the impacts the research has on them. This paradoxically rewarding and taxing work necessitates changes in methodological training and practice, institutional support, and an openness to innovation when calling for equity, diversity, and inclusion in the academy.
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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 | 0.366 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.021 | 0.242 |
| Scholarly communication | 0.032 | 0.030 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".