Bibliographic record
Abstract
Drawing on Althusserian notions of ideology and Bourdieu’s understanding of bodily hexis, A Body of Text seeks to reframe Physical Culture as an artifact worthy of serious study, more complex and less reactionary than its beefcake-and-sentiment reputation might suggest. This dissertation addresses the story of Physical Culture magazine from three different perspectives, reading the magazine through lenses of media history, medical history and social context, in order to understand the ways in which class operated on and through the body. In contrast to nearly every other publication in the early twentieth century, Physical Culture suggested that class mobility was possible, and that success would naturally follow improvement of body and health. Whereas the idea of “fitness” in the eugenics movement very clearly indicated an essential condition, consequent to the quality of the germline, Physical Culture initiated the idea that fitness was obtainable – and commodifiable – through diet, exercise and what we now call “lifestyle.” Much of this argument is constructed by contrasting Physical Culture with Hygeia, the health magazine created by the American Medical Association for a lay audience, and by contrasting Physical Culture impresario Bernarr Macfadden with his counterpart at the AMA, Morris Fishbein. Whereas Macfadden’s story has been told and retold (albeit in biographies that are increasingly interesting but not yet definitive), Fishbein’s story has yet to be the subject of a responsible biography. First gestures in that direction open the door to further work on Fishbein as a subject, and to deeper studies of the relationship between medicine, marketing and modern consumerism. Far from suggesting that Macfadden is ipso facto a liberatory force or a sophisticated theorist, the likeliest explanation for the complex, unstable and evolving constructions of body politics in Physical Culture are twofold: first, coming himself from “unfit” germlines, Macfadden needs to enrich contemporary thinking about the body to make sense of (and room for) his own success; second, and more importantly: you can’t sell a bloodline.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".