Family Friendly Workplace Benefits: The U.S., Canada, and Europe
Bibliographic record
Abstract
This article utilizes an eclectic, two dimensional political-economy perspective linking class power and organizational factors as the basis for analyzing variations in family friendly benefits provided to workers by U.S. employers and by state policies in six industrialized nations. An Organizational-Class Power approach is used to develop four hypotheses concerning benefit variations as influenced by the intersection of class power and organizational structures in the U.S. employer arena. In the state policy arena, a Collective Bargaining-Class Power approach is used to explore the relationship between the proportion of workers covered by collective bargaining agreements and variations in state mandated benefit levels. Data from the 1996-1997 National Organizational Survey (NOS) are used to test our four hypotheses. The NOS findings indicate that variations in U.S. employer-provided benefits are influenced by class-based power resources reflected in and conditioned by several organizational-level variables. Those variables which trend in directions predicted to enhance worker power within organizations are associated with significantly higher worker benefit levels in the employer arena. In the state policy arena, evidence from a variety of sources reveals a pattern whereby nations with higher proportions of workers covered by collective bargaining agreements mandate higher levels of benefits than those with lower proportions of workers covered by such agreements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".