Using a Mixed-Methods Approach to Examine the Expanding Reach of Body Classification into the Twenty-First Century
Bibliographic record
Abstract
Abstract This article uses a mixed-methods approach combining historical inquiry and quantitative science mapping techniques to examine the expanding reach of the somatotype body measurement and classification system, based on posture photography and anthropometric measurements, well into the twenty-first century. It illuminates the persistence of deterministic thinking about the relationships between physique and athletic ability in physical education and sports science, as well as how these views evolve. While William Sheldon, founder of the somatotype system, increasingly shifted his research focus toward criminal anthropology, physical educators throughout the second half of the twentieth century found the somatotype useful for assessing the physique and performance of Olympic athletes, often drawing on a priori racial and ethnic categories. By the early 2000s, somatotype research was becoming increasingly popular internationally, focused mainly on the measurement and assessment of sporting talent through drawing correlations between body type and sports performance and relying on commonsense assumptions about the innateness of athletic ability. Missing, however, was a deeper consideration of the potential mechanisms or pathways driving these relationships.
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 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 | 0.060 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".