Bibliographic record
Abstract
Th e World Health Organization (WHO) defi nes obesity as “abnormal or excessive fat accumulation that may impair health”1. Obesity is caused by an energy imbalance between calories consumed and calories expended- basically the individual is taking in more calories than they are expending. However, reducing obesity is not as simple as telling people to eat less and exercise more. Th ere are many factors that lead to obesity, tied together in complex relationships between physiology, individual behaviours and environmental factors at the family, community, national and global levels. Causes of obesity in the Aboriginal population First Nations, Inuit and Métis people in Canada have much higher rates of obesity and diseases associated with obesity such as diabetes, hypertension and heart disease. Rates of obesity are even higher for First Nations, Inuit and Métis children compared to the Canadian born non-Aboriginal population of children. Th ese high rates are related to a complex set of related factors that have been categorized by Willows et al (2012)2 as early life events, family feeding practices, food insecurity and colonization practices and policies. Th is group of researchers examined the many factors that contribute to high rates of childhood obesity in Aboriginal communities. Th ey developed an ecological model to help understand the complex interactive relationship between the many contributing factors, as well as recognizing the fact that colonization infl uences all levels of the model. The Individual An individual’s risk of becoming obese is directly impacted at several levels. Certain un-modifi able biological factors, such as age, sex and genes, may predispose an individual to obesity. Early life events can also lead to obesity such as having an overweight or obese mother, having a mother with diabetes, and not being breastfed. An individual’s knowledge and beliefs about healthy weight can also aff ect their likelihood of becoming obese. Th e probability is also impacted by psychological factors such as self-effi cacy, motivations and
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".