ROCK‐PAPER‐SCISSORS: PLAYING THE ODDS WITH THE LAW OF CHILD RELOCATION
Bibliographic record
Abstract
This article offers for inspection the proposition that the adversarial evidence‐based litigation process is unsuitable for resolving custody cases in general and relocation cases in particular. It analyzes the leading cases from New York, Massachusetts, California, England, Canada, and Australia. It reaches a conclusion that no jurisdiction has devised a legal standard or formula that enables a judge to predict the future best interest of a child if that child is allowed to relocate with one parent away from the other. For this reason, the court has a duty to offer as sophisticated and friendly a settlement process and atmosphere as possible. However, knowing that judges will still be required to resolve these difficult cases because they often seem impervious to settlement, the article offers thirty‐six factors that a court should consider in all move‐away cases. By relying on each of these factors that is relevant to the case, the parents will have an understanding of why the decision was made the way it was and it will also allow for effective appellate review.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".