Bibliographic record
Abstract
This book continues the themes addressed by its two predecessors in this mini-series by examining the role of the principle of the welfare interests of the child in the law of the U.S. and Canada. It provides a record of the key milestones in its development in each country and conducts a comparative analysis of the contemporary law relating to children in both. In doing so, it focuses also on the Indigenous communities – the AN/AI and the First Nations – of the U.S. and Canada respectively. By identifying and analysing the functions of the principle in the public (care, protection and control, etc), private (matrimonial, adoption, etc) and hybrid (adoption from care, surrogacy, etc) sectors of family law, it builds a picture of the law relating to children in the two countries and reveals significant jurisdictional differences. By examining the legislation and related caselaw, it assesses the different effects of the same legal framework on the welfare of Indigenous and other children. In addition to a digest of cases and legislation that identifies and tracks the role of this legal principle, lawyers, academics and other researchers will find a wealth of information on how it has evolved to reflect corresponding changes in social mores. For those interested in politics and social policy, there is much illuminating evidence of how the law has balanced this principle relative to others in both civil and criminal contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".