Bibliographic record
Abstract
Family courts are struggling to resolve parenting disputes over children’s treatment. These cases ask judges to decide such sensitive matters as whether a young person will be vaccinated against their wishes, granted access to gender-affirming healthcare, or forced into therapy. Parenting disputes over children’s treatment implicate two distinct and potentially conflicting areas of law: family law and health law. Because family law and health law employ different legal standards and espouse different legal principles, the outcome in these cases may depend on which legal framework is applied. This article makes two contributions. First, it surveys recent family court decisions and suggests that courts are resolving parenting disputes over children’s treatment in one of three ways: (1) applying health law rather than family law; (2) drawing on health law principles in applying family law; and, most commonly, (3) applying family law rather than health law. Second, I look to larger debates around children’s welfare versus autonomy to make a case for how the apparent tension between family law and health law in these cases may be reconciled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".