Measuring Success: An Evaluability Assessment for the Grand Forks Domestic Violence Court
Bibliographic record
Abstract
First implemented in the 1990s, specialized domestic violence courts represent one of several solutions developed to improve the response to domestic violence and enhance services for victims (Collins et al., 2021). Other solutions have included mandatory arrest and prosecutorial no-drop policies as well as increased funding support for victim services. There are reportedly over 300 DVCs in the United States as well as 50 in Canada and 100 in the United Kingdom (Eley, 2005; Gutierrez et al., 2016; Hemmens et al., 2020; Home Office, 2008; Tutty & Koshan, 2013). Based on input from a variety of key stakeholders including judges, state’s attorneys, public defense, court administration, and Community Violence Intervention Center (CVIC) staff in 2016, a specialized Domestic Violence Court (DVC) was formally established in Grand Forks (GF) in 2018. It is currently the only DVC court in the state. The GFDVC is a post-conviction specialty court whereby convicted individuals are required to participate in an orientation, intervention programming (such as New Choices facilitated by CVIC), and regular review hearings with Judge Jason McCarthy or Judge Jay Knudson. The goals of the program include increased communication and safety for victims as well as increased compliance and recidivism reduction for the perpetrators. This evaluability assessment briefly summarizes relevant outcome literature pertinent to DVCs, reports the current availability of data maintained by CVIC, and provides short-term and long-term recommendations.
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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 | 0.066 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".