Toward Active Reflexivity: Positionality and Practice in the Production of Knowledge
Bibliographic record
Abstract
ABSTRACT How should scholars recognize and respond to the complexities of positionality during the research process? Although there has been much theorizing on the intersectional and context-dependent nature of positionality, there remains a disjuncture between how positionality is understood theoretically and how it is applied. Ignoring the dynamism of positionality in practice has implications for the research process. This article theorizes one means of recognizing and responding to positionality in practice: a posture of “active reflexivity.” It outlines how we can become actively reflexive by adopting a disposition toward both ongoing reflection about our own social location and ongoing reflection on our assumptions regarding others’ perceptions. We then articulate four strategies for doing active reflexivity: recording assumptions around positionality; routinizing and systemizing reflexivity; bringing other actors into the process; and “showing our work” in the publication process.
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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 | 0.124 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.118 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".