Abused Women as ‘Alienating’ Mothers and Violent Men as ‘Good’ Fathers: Double Standards in Child Protection and Child Custody Proceedings
Bibliographic record
Abstract
ABSTRACT Drawing upon multiple case studies, this article examines how parenting double standards are reproduced in situations where women who have experienced domestic violence have been seen as ‘alienating’ mothers, while the men who have perpetrated the violence have been seem as ‘good’ or ‘good enough’ fathers. In total, 25 cases studies were conducted with women who had experienced domestic violence, had at least one child and had been perceived by at least one professional as ‘engaging in parental alienation’ at some point within the previous 5 years. Each case study involved at least one semi‐structured interview with the woman and the analysis of relevant documents, including family evaluation reports, child protection reports and court decisions. These double standards were identified when the mothers' and the fathers' behaviours and circumstances had been measured or evaluated using a different set of principles. The research findings reveal different main manifestations of parenting double standards, which relate to the parents' credibility and the parents' past and personal histories. Moreover, the fact that the parents talk to the children about the other parent is perceived differently when mothers and fathers are doing it. Professionals also reproduce double standards when they minimize and the justify the parents' ‘inappropriate’ behaviours and when they consider the parents' help‐seeking strategies.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".