Intersectional Performativity Framework: A Collective Reflexivity in Ethics on Ethnographic Research With Vulnerable Groups in China
Bibliographic record
Abstract
This study examines the ethics of qualitative research with vulnerable populations by shifting from procedural compliance to an intersectional performativity approach that integrates intersectionality and performativity. Traditional ethical frameworks, largely shaped by Global North institutions, prioritize standardized and procedural protocols that may overlook local contexts and the fluid positionalities of researchers. Drawing on collective reflexivity from three ethnographic projects with lower-socioeconomic-status (SES) children and families in China, this study investigates two questions: (a) How do we perform as researchers in relation to the intersectional identities of ourselves and the participants? and (b) How do we as researchers navigate ethical issues in our performative acts during fieldwork? Findings show that researchers’ identities are continually enacted and negotiated within specific, context-bound interactions. Researchers often adopted a “tutor” role, simplifying language and downplaying educational credentials to minimize social distance from lower-SES children. They also engaged in culturally embedded practices of courtesy, including modest gift-giving and reciprocal favors, to establish trust and maintain research access. In addition, to navigate socioeconomic disparities, researchers consciously muted elite markers by avoiding expensive clothing or references to privileged experiences. Gendered self-monitoring further shaped fieldwork, as female researchers adjusted their behavior and attire in response to local gender norms. These performances were co-constructed, with participants sometimes subtly challenging researchers’ assumed roles. The study thus proposes an Intersectional Performativity Framework with reflective questions to help researchers examine how identities are performed, what assumptions underlie these performances, and how ethical considerations surface in research encounters.
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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 | 0.121 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.019 | 0.064 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".