GROUNDING ACCESS TO JUSTICE THEORY AND PRACTICE IN THE EXPERIENCES OF WOMEN ABUSED BY THEIR INTIMATE PARTNERS
Bibliographic record
Abstract
For women seeking to extricate themselves from the web of entrapment woven together by the multiple threads that make up the coercive control repertoire of their abusive intimate partners, it is often difficult to avoid engagement with legal systems. Yet, the legal systems they encounter—criminal, family, child welfare, immigration among them—are frequently unwelcoming (if not hostile), controlling, demeaning, fragmented and contradictory. While there has been a recent explosion of interest in “access to justice,” little attention has been paid to how we might conceptualize access to justice in a manner that speaks meaningfully to the circumstances of women who experience abuse in their intimate relationships. For such women, access to justice is curtailed not only by lack of representation, delays, costs, and procedural complexities—the obstacles commonly associated with access to justice failings—but by three inter-related phenomena: the enduring hold of an incident-based understanding of domestic violence; the failure of legal actors to curb men’s strategic use of legal systems to further their power; and the host of complications—contradictory expectations, inconsistent orders, repetitious proceedings, sweeping surveillance—that arise when women are compelled to navigate multiple intersecting legal systems. What is required, I argue, is a conceptualization of access to justice that places women’s safety and well-being at its core.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".