From “Buzzword” to Best Practice: Applying Intersectionality to Children Exposed to Intimate Partner Violence
Bibliographic record
Abstract
Empirical studies on the impact of intimate partner violence (IPV) on children have burgeoned over the last three decades. Notably absent from existing approaches to studying children exposed to IPV, however, is attention to how various positionalities intersect to impact the experiences of children and their families. In fact, while the importance of an intersectional framework for understanding IPV has been discussed for over two decades, little or no attention has been given to issues of children's exposure to IPV. In this article, we examine the current state of the literature on children exposed to IPV through an exploratory meta-analysis, finding limited application of intersectionality and a focus on discrete categories of difference. We then demonstrate why and how an intersectional framework should be applied to children exposed to IPV, with specific strategies for research and policy. We suggest a child-centered approach that recognizes diversity among children exposed to IPV, extending the challenge to traditional "one-size-fits-all" models to include an intersectionality-informed stance.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".