Women and Children First, but Only If the Men are Union Members: Hiring Halls and Delinquent Child-Supporters
Bibliographic record
Abstract
A widely-held opinion is that, in a court of law, women almost always lose – and so do labor unions. But when three disfavored groups petitioned to a judge, the male union member lost his case. Arguably women and children have suffered the most and longest legal disabilities under state law. Until mid-twentieth century, women were unable to hold or inherit property, bring lawsuits, or manage their own business affairs. Nor were they able to testify against their husbands in any trial. There were few, if any, laws against marital assault – or even rape, until the 1960’s. Their civil rights in employment, public accommodations, education and military service were not legally established until the last quarter of the 20th century. Children remain legally incompetent to enter contracts, bring suit, make family decisions, or enjoy the franchise. Only when their acts are deemed criminal are they treated the same as adults. In family law and domestic abuse cases, courts have historically favored husbands and fathers over mothers and offspring. Child-support enforcement has been traditionally law. But in a case demanding the cooperation of union pension fund managers in child support collections, a court forced to choose among three less favored parties, traditional hierarchies were altered, institutional discrimination trumped ordinary common law preferences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".