Bibliographic record
Abstract
Abstract Over the past five decades, welfare state research has largely focused on two social policy pillars: income programs (the first pillar) and social services (the second pillar). This chapter highlights the importance of protective legislation as an under-researched third pillar of the welfare state comprising networks of laws, legal acts and codes, regulations, and rights. Present across virtually all social policy domains, third pillar measures include minimum wage laws and workplace health and safety regulations, rent control laws and eviction protection legislation, child protection laws and corporal punishment bans, as well as webs of legislation prohibiting discrimination on the basis of gender, sexual orientation, “race,” ethnicity, nativity, religion, and ability. Although critical to promoting and securing our welfare, they are infrequently examined as key components of welfare states and rarely invoked to distinguish welfare states or to establish, inform, refine, or challenge dominant welfare state models and typologies. Drawing on cross-national contrasts, this chapter suggests that the third pillar of Canada’s welfare state is, like its other two pillars, comparatively underdeveloped in several policy areas. It also underscores the importance of policy coordination, highlighting what Canada can learn from other countries that have done this more effectively. This is especially crucial when addressing complex composite issues and problems such as child well-being. The closely interwoven concerns and challenges faced by children and other vulnerable and structurally disadvantage groups are most effectively addressed through coordinated and coherent networks of policies across pillars and relevant domains.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".