Institutions, constitutions, actor strategies, and ideas: Explaining variation in paid parental leave policies in Canada and the United States
Bibliographic record
Abstract
This article analyzes the reasons for cross-national differences in the legal and policy regimes related to maternity and parental leave in Canada and the United States. It explores why the Canadian federal government implemented a paid maternity leave policy much earlier than the U.S. It also explores why the U.S. had no national parental leave provisions before 1993 and why it does not have national paid leave provisions now. The article weighs the impact of institutional, constitutional, agential, and ideational/normative factors to explain policy differences in the two countries. While key institutional structures, such as federalism, and key court decisions have shaped maternity and parental leave policies substantially in the two countries, an important part of the explanation for the cross-national differences lies with how interests mobilize in response to past policy actions and legal norms and traditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".