Engaging Canadian youth in conversations
Bibliographic record
Abstract
Currently, youth spend less time being physically active while engaging in more unhealthy eating behaviours than ever before. High rates of unhealthy behaviours such as physical inactivity (Active Healthy Kids Canada 2011; Freeman et al. 2011), unhealthy eating (Butler-Jones 2008) and tobacco use are placing Canadian youth at risk of health problems such as increased levels of overweight and obesity, cardiovascular disease and type 2 diabetes (Morrison, Friedman & Gray-McGuire 2007; Vanhala et al. 1998; WHO 2012). It is estimated that obesity rates in Canadian children and adolescents have increased threefold in the last two decades (Active Healthy Kids Canada 2011). In 2010, 30 per cent of youth in Prince Edward Island (PEI) were overweight and obese and that rate had remained stable between 2008 and 2010 (Murnaghan 2011). Further, in line with Canadian averages (Roberts et al. 2012), only 45 per cent of youth in PEI currently meet the national daily physical activity guidelines (Murnaghan 2011), as recommended by the Canadian Society for Exercise Physiology. It is important to consider the school context when examining the health behaviours of youth, as they spend a large portion of their time in that environment. In relation to the school context and health-specific programs and policies currently implemented in PEI, the provincial government regulates the nutrition policy that is implemented in all schools in the province and manages the physical education and health curriculum. However, the schools are fairly autonomous, in that approaches taken to meet educational outcomes, as set by the province, can be dynamic and specific to each individual school. Schools take it upon themselves to initiate or implement healthrelated programs or school policies beyond those outlined by the provincial government or school board/district. For example, if a school identifies bullying as an issue of concern, with support from the provincial government and school board, the school takes the initiative to implement a program or policy to work through and resolve the issues. The provincial government fully Gateways: International Journal of Community Research and Engagement Vol 7 (2014): 85–100 © UTSePress and the authors
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".