Gender and child custody outcomes across 16 years of judicial decisions regarding abuse and parental alienation
Bibliographic record
Abstract
There have been legislative efforts to control how child custody decisions are handled in family courts where allegations of abuse and of parental alienation (PA) are levied. The “findings” reported to support such legislation have been based on one unreviewed study with identified methodological issues (Harman & Lorandos, 2021). We tested six pre-registered hypotheses to determine whether there is empirical support for the “research findings” used to support these laws. Five-hundred PA cases were sequentially selected from 4,889 Canadian trial court decisions. Independent coders who were blind to the hypotheses coded all cases for details about custody and allegations of abuse. We failed to find support for the “findings” that have been used to support legislative changes. For example, this study focused only upon cases where PA was determined to actually have occurred in at least one of the children in the family. It differs from Harman & Lorandos (2021) in that this study found that alienating mothers’ claims of abuse against known “abusive” alienated fathers were not being discredited more often than they were for alienating fathers. The negative impact of failing to base legislation on a comprehensive consideration of the full scope of scientific evidence available (e.g., Kayden’s Law in the reauthorized Violence Against Women’s Act, 2022) is discussed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".