Whiteness in the Qualitative Research Setting: Critical Skepticism, Radical Reflexivity and Anti-racist Feminism
Bibliographic record
Abstract
This essay critically reflects on challenges and dilemmas I encountered when interviewing white women about their experiences with gender, racialization, and practices of whiteness. These challenges and dilemmas in the research setting relate to the researcher-participant relationship and, in particular, participants’ use of 1) a “rhetorical ethic,” in which their social justice narratives were contradicted by demonstrations of their own racist ideologies; and 2) how whiteness and femininity were sites of power and resources for “social desirability bias” and impression management in response to my positionality as a white woman with a Black spouse and two racially mixed children. Additionally, this essay grapples with the emotionally difficult journey of being a researcher with the feminist commitment of “giving voice” to women by developing a bond of mutual trust, while at the same time feeling compelled to conceal oneself in search of “honest” responses from the research participants. This reflection illuminates how a/symmetries of power between researcher and the researched are inscribed with race and gender dynamics that are not always discernible, yet have a tremendous influence on data gathering. These dynamics require recognizing the agency of the research participants to shape what are considered and interpreted as data. These dynamics also require treating the data with “critical skepticism” and subjecting the participants’ responses to a “radical reflexivity” rooted in understanding how the larger social, political and historical “facts of whiteness” inform the microcosm of the researcher-participant relationship.
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 | 0.248 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.031 | 0.146 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".