Bibliographic record
Abstract
In 2015, in response to a rising problem of gun violence in the City of Wilmington, Delaware, and due to the urging of local public officials, the Centers for Disease Control and Prevention (CDC) conducted a groundbreaking study around the public health crisis and issued three recommendations on opportunities for prevention. The ideal solutions centered on the creation of a predictive analytic tool that would help social service providers determine who is most likely, based on a set of weighted risk factors, to engage in gun violence. As various entities started to lay the foundation for implementing the CDC's recommendations, they faced several hurdles directly related to this new technological solution. After careful consideration and thorough vetting, which was also recommended by the CDC, led by Governor Carney's Family Services Cabinet Council (FSCC), Delaware concluded two things: a tool of this nature presents ethical issues, and there are evidence-based strategies to identify those engaging or likely to engage in gun violence; and notwithstanding the ethical concerns surrounding the tool, Delaware lacked the technology infrastructure and staffing to develop such a tool. Ultimately, good collaboration (facilitated by Social Contract, a local consulting firm) through the FSCC fostered an alternate path forward in keeping with the spirit of the CDC's recommendations; while the CDC's recommendations were not precisely enacted, their contribution has led to investments and capacity building in Delaware to support individuals and families most proximal to the problem. Ultimately, convening stakeholders to fully examine an issue and ideate solutions with the most potential for impact resulted in two meaningful outcomes: (1) an innovative approach to ultimately reduce gun violence in the City of Wilmington through widespread collaboration of state services, developing meaningful relationships with those directly engaged in gun violence; and (2) the creation of a statewide data-sharing system that will help improve service delivery and outcomes for Delawareans in need.
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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 | 0.197 | 0.487 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.025 | 0.054 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.028 | 0.041 |
| Insufficient payload (model declined to judge) | 0.042 | 0.024 |
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".