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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".