Why are some Children Left Out? Factors Barring Canadian Children from Participating in Extracurricular Activities
Bibliographic record
Abstract
<b>Abstract</b><p>Using three waves of data from the Canadian National Longitudinal Survey ofChildren and Youth, this study examines the impact of child, family andcommunity level characteristics on children’s participation in extracurricularactivities between the ages of 4 and 9 (n=2,289). Results show a large positiveeffect of family income on children’s participation in structured activities.Living in a poor neighbourhood constitutes an extra disadvantage for children'sparticipation in organized sport activities. Our study also identifies a positiveassociation between parent’s education and children’s participation in mostactivities, and a negative association between family size and some structuredactivities. Furthermore, children of immigrants, as well as children of visibleminority and aboriginal children were found to be disadvantaged in theirparticipation in some activities.<p><b>Résumé</b><p>Sur la base de trois cycles de données de l’Enquête canadienne longitudinalesur les enfants et les jeunes, cette étude examine l'impact des caractéristiques del'enfant, de la famille et de la communauté sur la participation des enfants de 4à 9 ans (n=2,289) dans des activités parascolaires. Les résultats démontrent unfort effet positif du revenu familial sur la participation dans des activitésstructurées. Vivre dans un quartier pauvre constitue un désavantagesupplémentaire pour la participation des enfants dans des activités de sportorganisé. Notre étude identifie aussi une association positive entre la scolaritédes parents et la participation des enfants à la plupart des activités, et uneassociation négative entre la taille de la famille et certaines activitésstructurées. De plus, on a trouvé que les enfants d'immigrants, de même que lesenfants de minorités visibles et les enfants autochtones étaient désavantagés parrapport à leur participation dans certaines activités.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 teacher head, 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".