Helping People Change: Promoting Politicised Practice in the Health Care Professions
Bibliographic record
Abstract
While this is an obviously contentious statement, we take this as the starting point for our analysis and efforts to expand the currently truncated obesity debate beyond its medicalised and reductionist focus on disease, risk and pathology. In saying that there is no such thing as obesity we don’t mean to imply that no one is fat. What we are saying is that the term ‘obesity’ — and more especially its assumed precursor ‘overweight’ with which it is often conflated — as currently used in the clinical and academic worlds with which we are familiar has little medical salience. For a very high percentage of populations reputedly in the grip of an ‘obesity epidemic’ (the United Kingdom, the United States and Canada for example) fatness and/or heavy bodyweight (taken as indicating overweight or obesity) do/does not, as is popularly promulgated, reliably indicate a person’s metabolic risk, except at extremes of the weight spectrum. By metabolic risk we refer to the metabolic dysregulation arising from a range of lifecourse experiences that can predispose people to diabetes, hypertension and cardiovascular disease. Belief in obesity (and overweight) couples weight and high metabolic risk as intrinsically related variables and thereby perpetuates what we, in our role as health professionals and critical weight scholars, view as a harmful conglomerate of inappropriate interventions premised on equally harmful ideological drivers. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.109 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.040 | 0.097 |
| Scholarly communication | 0.036 | 0.030 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.033 | 0.046 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".