Bibliographic record
Abstract
Over the past twenty years, child and family policy has become one of the most active areas of Canadian social policy.However, our understanding of whether children's lives have actually improved is limited.This dissertation addresses this research gap in three papers that examine the politics of Canadian child policy, evaluate child policy outcomes, and explore the determinants of child well-being that policy attempts to affect.The first paper examines the evolution and politics of child policy in Canada from 1995-2015.During this period, federal government spending targeted to families with children has more than doubled in real terms with a focus on increasing family income.To explain this focus, I identify four perspectives on the child in the Canadian political discourse: child as family responsibility, child as deserving poor, child as rights bearer, and child as investment.I argue that the scope of child policy in Canada has been constrained to a family income paradigm that sits at the intersection of these perspectives.The second paper evaluates the significant expansion of Canadian child benefits from 1994 to 2010.Using data from the National Longitudinal Survey of Children and Youth (NLSCY) and the Survey of Labour and Income Dynamics, I compare measures of poverty and inequality with indicators of young children's well-being.I find young Canadian children have not seen significant change in their relative economic position.Indicators of children's physical, emotional, developmental well-being and family context remain essentially stable, as do gaps between the outcomes of poor and rich children.Understanding how household income and socioeconomic (SES) status affect child outcomes is crucial for designing effective public policies to reduce the disadvantage of growing up in poverty.In the third paper, I use structural equation modeling of NLSCY data to disentangle the different pathways of income's and other elements of SES's effect on children's cognitive, socio-emotional, and behavourial outcomes.Applying theories of family stress and family investment, the analysis examines children's activities, parenting, and family functioning as potential mediators.I find that SES has a consistently larger effect on child outcomes than income, with parenting emerging as a key mediation pathway.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".