Bibliographic record
Abstract
I have argued that the medicalization of consumer choice is implicit in the obesity debates. As a media analyst, it is impossible to ignore the role that medical rather than communications researchers played in study of TV as a lifestyle risk. Nutrition and kinesiology are the two health sciences that have contributed most to the medical establishments’ diagnosis of the underlying lifestyle risk factors contributing to weight gain — vying with each other to set the ‘risk agenda’ by explaining to what degree fast food or sluggish kids were most responsible for weight gain. However, in heavy TV viewing both nutritionists and kinesiologists found a risk factor which they agreed on. Dietz and Gortmaker’s (1985) epidemiological research on over 6000 12–17-year olds was one of the first to find that the likelihood of ‘overweight’ in adolescent populations increased by 2 per cent for each hour of television viewed. This hallmark study concluded that television viewing is a ‘major health concern at which counselling should be directed’ because it promoted both increased food consumption and reduced activity. The relationship between amount of TV viewed and obesity persisted when controlled for prior obesity, region, season, population density, race, socioeconomic class and a variety of other family variables implying that it was a risk factor independent of all other population variables.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".