Bibliographic record
Abstract
In recent decades, there has been a dramatic increase in unhealthy weight for both children and adults. The Canadian standard of living has changed in favour of more easily prepared, calorie-dense foods and sedentary practices. Many family characteristics have also changed over the past 50 years. More Canadian families are living in disadvantaged situations, forecasting a host of unhealthy behaviours and attitudes in adults. The poor are not only getting poorer, they are also becoming heavier. Children from disadvantaged families seem to be leading the trend in increasing prevalence of unhealthy weight. Because they live in neighbourhoods that are perceived as unsafe, these children are likely spending more time indoors. This is associated with watching more television, which not only displaces other forms of educational and active entertainment but also places them at risk of learning inaccurate information about proper eating. Social science research helps identify factors contributing most to the rise in excess weight within this population, thus providing essential clues for effective approaches to its eradication. Ces dernières décennies, on remarque une forte augmentation du poids malsain, tant chez les enfants que chez les adultes. Le mode de vie favorise désormais des aliments riches en calories et plus faciles à préparer, ainsi que des habitudes sédentaires. Les caractéristiques de nombreuses familles se sont également modifiées depuis 50 ans. Plus de familles canadiennes sont défavorisées, ce qui présage toute une série de comportements et d'attitudes néfastes pour la santé à l'âge adulte. Non seulement les personnes pauvres s'appauvrissent-elles davantage, mais elles prennent également du poids. Les enfants de familles défavorisées semblent ouvrir la voie à la prévalence croissante du poids malsain. Puisqu'ils habitent dans des quartiers perçus comme dangereux, ces enfants sont plus susceptibles de passer beaucoup de temps à l'intérieur. Pour cette raison, ils passent plus de temps devant la télévision, qui détrône d'autres formes de divertissements éducatifs et actifs et risque de leur transmettre de l'information inexacte au sujet d'une saine alimentation. Les recherches en sciences sociales contribuent à établir les facteurs les plus responsables de cette augmentation de l'excès de poids au sein de cette population, ce qui fournit des indices essentiels pour l'élaboration de démarches efficaces à leur éradication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".