The body mass index: What’s the use?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it