Bibliographic record
Abstract
Cet article offre un portrait statistique du bien-être économique des enfants au Canada. Les auteurs examinent les transformations qu'on subies les familles, au niveau du pays et par province. Ils montrent également comment les politiques canadiennes destinées aux enfants ont changé et comment elles varient selon les régions. Dans la partie principale de l'étude, ils analysent ensuite les changements ou les différences en matière de revenus médians, de distribution des revenus et de pauvreté chez les enfants, à la fois avant et après impôts et transferts, à différents moments dans le temps, pour différents types de familles et dans les différentes provinces. Enfin, ils comparent le bien-être économique des enfants du Canada à celui des enfants de huit autres pays riches. Abstract: This article provides a statistical picture of the economic well-being of Canadian children. We discuss changes in families, nationally and by province. We outline how Canadian policy in support of children has changed and how it differs across regions. Changes or differences in median incomes, in income distributions and in child poverty both before and after taxes and transfers, at different points of time, in different kinds of families, and in different provinces constitute the core of the article. Finally, the economic well-being of Canadian children in 2010 is compared with that of children in eight other affluent countries.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".