The body mass index: What’s the use?
Bibliographic record
Abstract
The body mass index (BMI) is a ubiquitous metric frequently used in body image research: as a correlate, covariate, descriptor, and more. However, the racist history of the measure is often unknown or unacknowledged. BMI was coined by Ancel Keys who used Adolphe Quetelet's statistics of weight and height, later becoming a measurement of so-called "health." Eugenics founder Francis Galton used Quetelet's statistics to determine the abnormal, in a concerted effort to eliminate bodies seen as "unfit." The BMI has been used to compare bodies to white masculinist ideals for decades (e.g., in insurance coverage, healthcare access), which is something body image scholars must reckon with if our collective goal is to subvert unrealistic, harmful, and damaging beauty ideals-not inadvertently validate them. In body image research to date, BMI use/usefulness helped unpack the complex relationship between negative and positive body image(s): BMI is consistently related to both. However, it has also been overused, and we argue-uncritically and inappropriately used-since it misses the root issue: fat discrimination and weight stigma. Thinking with critical race theorist Sara Ahmed's (2019) work on "use," we open a conversation on the potential implications of use/disuse of BMI. We outline the use, usefulness, and used-upness of BMI and offer reflections on what it means to be a critical user or outright refuser of this metric.
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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 | 0.042 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".