Positioning Positionality and Reflecting on Reflexivity: Moving From Performance to Practice
Bibliographic record
Abstract
Researcher reflexivity and acknowledgement of positionality are emerging as key concepts for evaluating the quality of qualitative research. Collectively, we explore the relationship between reflexivity and positionality statements as reflexive practice, considering who benefits, who has authority, and our expectations of each other as qualitative researchers. Moving between examples of doing reflexivity in practice and what is often requested of authors during the peer review and editorial processes, we challenge the idea that positionality statements in the form of identity disclosures ought to be taken as the token performance of reflexive work, despite their frequent use as such. We begin by outlining the role and purpose of reflexivity in qualitative research and follow by examining the turn toward identity disclosure as fulfilling this purpose. Following, we examine the ways in which a "shopping list" positionality statement can create disproportionate risk, reinforce stereotypes, and homogenize researchers identifying with marginalized groups, without necessarily benefiting the research process or how research is communicated. In addition, we present alternative ways of doing and communicating reflexivity in qualitative research that, although not without their own challenges, allow reflexivity to take up the space it deserves during the research process and dissemination.
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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 | 0.630 | 0.644 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.022 | 0.150 |
| Scholarly communication | 0.050 | 0.057 |
| Open science | 0.007 | 0.043 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".