Reflections on the Domestic Violence Disclosure Scheme (England and Wales)
Bibliographic record
Abstract
The Domestic Violence Disclosure Scheme (DVDS) aims to reduce harm through improving access to background information for people with concerns about a romantic partner’s behaviour. This reduction is predicated on the disclosure recipient taking steps to ensure their safety, either by managing the situation or ending the relationship. As fewer than half of the thousands of annual applications result in disclosures, and no information is held about any subsequent steps taken by applicants or recipients, it is unclear whether or not the DVDS is actually reducing domestic violence. Nonetheless, Scotland and Northern Ireland have implemented their own variations of this policy, as have some Canadian and Australian states. This policy analysis draws on empirical research into the DVDS in terms of its national and local operation in order to assess the strengths and limitations of its capacity to reduce harm. The analysis outlines how the policy may be difficult to access; deflect – rather than prevent – harm; shift safeguarding responsibilities onto the most vulnerable; and be incorrectly interpreted in terms of outcome. The paper makes recommendations for improvement in order to enhance the policy’s efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".