House Rules: Changing Families, Evolving Norms, and the Role of the Law
Bibliographic record
Abstract
The paradigm of family has shifted rapidly and dramatically, from nuclear unit to diverse constellations of intimacy. At the same time, some norms resist change, such as women’s continuing role as primary care providers despite their increased uptake of paid work. This tension between transformation and stasis in family arrangements has an impact on economic, emotional, and legal aspects of daily life. House Rules critically explores the intertwining of norms and laws that govern familial relationships. The authors in this incisive collection engage with four countries – Canada, the United States, the United Kingdom, and Taiwan – and expose the ingrained and unsettled norms that affect families and the law’s role in regulating them. They reveal the assumptions that create inequality and animate legislation, evaluating the effects of laws and scrutinizing reforms. Over recent decades, the law has struggled to adjust to transformations in what typifies the structures and practices of family life. House Rules provides tools to analyze those difficulties and, ultimately, to design laws to better respond to ongoing change and avoid entrenching inequalities. Family law scholars, gender studies and feminist scholars, and sociologists of the family will all find this a valuable and informative work. [From UBC Press | House Rules - Changing Families, Evolving Norms, and the Role of the Law, Edited by Erez Aloni and Régine Tremblay]
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".