Fat Studies and Arts-Based Approaches: Positioning the Need for “Movement”
Bibliographic record
Abstract
Fat studies is a field of study that provides critiques and disrupts western biomedical assumptions about fatness. Various methodologies are taken up within the field of fat studies; however, arts-based methods offer unique and distinct methodological insights to the field. Addressing these important intersections, this methodological paper illuminates three broad arts-based research methods used in fat studies and how these methods highlight important themes of disruption, space, and temporalities. In particular, the importance of temporalities and time captured paper through arts-based methods covered are noteworthy. In the space of fat temporality, we end with a critical invitation for fat studies and arts-based scholars to innovate work using the method of performance art, specifically how performance can facilitate unique insights to create new knowledge at the intersection of fat studies and arts-based research.
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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 | 0.091 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.115 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".