‘Parental alienation’ allegations in the context of domestic violence: impacts on mother-child relationships
Bibliographic record
Abstract
Despite the multiple critiques that have been addressed to the concept of ‘parental alienation’ , evidence suggests an increased popularity in recent years. Abused women who seek protection can be labelled as ‘alienating’ mothers. This article investigates the impacts of ‘parental alienation’ allegations on women and children who have experienced domestic violence, focusing on the impacts on mother-child relationships. It draws upon findings from a study that involved multiple case studies conducted with women who had experienced domestic violence and who had been seen as ‘engaging in parental alienation’. For these women, the allegations of ‘parental alienation’ had had negative and sometimes dramatic impacts on the relationships with their children. First, the allegations had resulted in limited or no contact between women and their children. Second, the allegations had limited mother-child communication and had made it more complicated. Third, the allegations had limited women’s ability to protect their children. Fourth, some women reported changes in their children’s behaviours, as some children had expressed feelings of frustration and anger as a result of this situation. The fifth theme emerging from the data was a collection of strategies women used to protect and support their children despite these allegations.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".