Bibliographic record
Abstract
It is not uncommon to find educational literature that links poor nutrition to lowered performance on school related tasks and to a variety of behavioural and wellness concerns. Media speaks consistently to the issue of Canadian children who arrive at school either underfed or poorly fed. Nutrition experts refer to what is contained in many lunch bags, or what foods children consume as snacks, as not meeting Canada's Food Guide recommendations. Food served in school cafeterias is often described as less than adequate. Even if children are provided with a proper breakfast and a healthy lunch often they refuse to eat what is provided and supplement with junk food options. To add fuel to the fire, prior to October 2004, elementary school children were able to purchase junk food through vending machines at school. With the passing of significant legislation by the Ontario government, that is no longer the case. However, while vendors are obliged to provide better nutritional choices it does little to alleviate the problem that many school children arrive at school either underfed or not fed at all .
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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 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".