Employing Polyethnography to Navigate Researcher Positionality on Weight Bias
Bibliographic record
Abstract
Researchers often focus on the content of their research interests but, depending on the research approach, may pay less attention to the process of locating themselves in relation to the research topic. This paper outlines the dialogue between an interdisciplinary team of researchers who were at the initial stages of forming a research agenda related to weight bias and social justice. Using a polyethnographic approach to guide our discussion, we sought to explore the diverse and common life experiences that influenced our professional interests for pursuing research on weight bias. As a dialogic method, polyethnography is ideally suited for the reflexive work required of researchers seeking to address issues of equity and social justice. Beyond more traditional approaches such as journaling, personal interviews, or researcher notes, the intersubjectivity highlighted by this method affords a richer space for exploration, challenging ideas, taking risks, and collectively interrogating both self and society. Following a discussion of positionality, the dialogue between researchers is presented, followed by their critique of the discussion, informed by professional literature.
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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 | 0.094 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".