Bibliographic record
Abstract
On 10 October 2006, BBC Breakfast Television reports that England is now officially the fattest nation in Europe. According to a Health Survey for England, 2003, 23% of English women and 22.2% of English men are obese. An official “fatness map” of the UK demonstrates that the West Midlands has the highest percentage of obese women (as determined by a body mass index over 301) and Yorkshire has the highest percentage of obese men. Later the news notes the alarming rates of eating disorders in Brazil; in a nation where thousands of people die for lack of proper food, others are voluntarily starving, binging and purging to obtain a certain look. The BBC breakfast show also airs an item about new “toy” exercise equipment designed to make children exercise. This equipment is modelled closely on its adult equivalents – treadmills, weight training machines – just smaller in size. When parents were asked why they would invest in such toys, they say they don’t want their children to become fat and unhealthy adults and therefore, they want to teach a healthy lifestyle from early on. While these parents appear well-informed about the importance of exercise, when I open my faculty website I find a special feature on why governmental messages on healthy lifestyle are not reaching their target: the fat and unfit. According to this report, the health messages preach to the already-converted while the couch potatoes stay horizontal unless harsh, negative words like “lazy” are used to hit home the message for the necessity of behavioural change. This “newsflash” does not detail why exactly there is a need for a behavioural change toward more active living. As I gather my mail, I read from the recent IDEA Fitness Journal (a US-based publication for fitness professionals) that medical costs for obese employees are 77% higher than for healthy-weight employees and that obesity-related disabilities cost employers up to $8,720 per claimant per year. It further estimates that obesity accounts for 43% of all healthcare costs by US business on coronary heart disease, type 2 diabetes, hypertension, hypercholesterolemia, stroke, gallbladder disease, osteoarthritis of the knee and endometrial cancer combined. These figures are designed to demonstrate to consumers the need to control obesity levels: being fat is costly and dangerous.2
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 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".