The use of parental alienation constructs by family justice system professionals: A survey of belief systems and practice implications
Bibliographic record
Abstract
Abstract Parent–child contact problems (PCCP) after separation and divorce are the focus of heated debate in academia and the popular media as to how best to identify, assess and respond to children who resist or refuse time with a parent. Practitioners disagree about the extent to which parental alienation (PA) is a valid and widespread phenomenon versus a legal strategy to counter IPV and child abuse allegations. This study sheds light on prevailing attitudes by surveying the opinions and beliefs of 1049 interdisciplinary family law professionals who deal directly with these matters in practice. These experienced practitioners were confident about their understanding of PCCPs despite little formal instruction on relevant issues. They were less clear about the differentiation between similarly used terms, research evidence, and interventions to address the problems. Emergent themes provide insight into practitioners' beliefs about the harm caused by a parent's alienating behaviors, the extent to which PA is a real phenomenon vs. a litigation strategy, the quality of social science empirical evidence, views of the child in PA cases, and recommended interventions. Responses demonstrate practitioners taking moderate positions that balance competing interests, struggling with ambiguities and contradictions rife in PA cases and PA‐related practice in family law.
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".