The impact of poverty on educational outcomes for children
Bibliographic record
Abstract
Over the past decade, the unfortunate reality is that the income gap has widened between Canadian families. Educational outcomes are one of the key areas influenced by family incomes. Children from low-income families often start school already behind their peers who come from more affluent families, as shown in measures of school readiness. The incidence, depth, duration and timing of poverty all influence a child's educational attainment, along with community characteristics and social networks. However, both Canadian and international interventions have shown that the effects of poverty can be reduced using sustainable interventions. Paediatricians and family doctors have many opportunities to influence readiness for school and educational success in primary care settings. Depuis dix ans, l'écart des revenus s'est creusé entre les familles canadiennes, ce qui est une triste réalité. L'éducation est l'un des principaux domaines sur lesquels influe le revenu familial. Souvent, lorsqu'ils commencent l'école, les enfants de familles à faible revenu accusent déjà un retard par rapport à leurs camarades qui proviennent de familles plus aisées, tel que le démontrent les mesures de maturité scolaire. L'incidence, l'importance, la durée et le moment de la pauvreté ont tous une influence sur le rendement scolaire de l'enfant, de même que les caractéristiques de la communauté et les réseaux sociaux. Cependant, tant au Canada que sur la scène internationale, il est possible de réduire les effets de la pauvreté au moyen d'interventions soutenues. Les pédiatres et les médecins de familles ont de nombreuses occasions d'agir sur la maturité et la réussite scolaire dans le cadre des soins de premier recours.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".