Our public safety system is a perfect storm
Bibliographic record
Abstract
Public safety results from an effective interaction of three separate systems - public health, mental health, and policing. In too many communities today, the crisis in our mental health system creates the perfect storm. Solving the issues of silo-based care necessitates creating an oversight data management structure supporting cross-sector data integration on all levels ensuring that both operational and technical frameworks exist to maintain the security of client data. Safer communities isn't just about being sophisticated, technologically advanced but using an intensifying laser-focus analysis on harmful criminality, and on stakeholders responsible for delivering to those in need of mental health services. The recommendations note that no one agency can solve the crisis in public safety alone. There is an urgent need for one new coordinating agency - a department in government - to eliminate the silos and act like a well-coordinated and effective service delivery network, with public commitment to outcomes and corresponding public accountability.
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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.033 | 0.040 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".