Developing a Theoretical Framework to Discuss Mothers Experiencing Domestic Violence and Being Subject to Interventions: A Cross-National Perspective
Bibliographic record
Abstract
The discourse about domestic violence has developed in patriarchal societies, and so we position our understanding of ‘mother’ within a patriarchal framework. We explore the ways in which ‘mothering’ and ‘mother blame’ have been constructed within that framework and how this becomes relevant in the context of domestic violence and child welfare social work. We review literature from Australia, Canada, England and Wales, and the United States of America that has focused on child welfare responses to mothers experiencing domestic violence and abuse. On the basis of that review, we argue that mothers are responsibilised for violence and abuse they do not perpetrate. We show that the way legislation operates in some jurisdictions facilitates hegemonic, patriarchal constructions. We call for a review of current child welfare social work policy and practice in which domestic violence is present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".