Municipal community safety and well-being policies: Intersectoral action as social innovation
Bibliographic record
Abstract
Municipal community safety and well-being policies or frameworks focus on public safety and well-being by design, but scope, focus, and operationalization vary. While municipal governments set priorities for safety and well-being, many contributing factors fall under the jurisdictions of other regional, provincial, or national actors across sectors. This complex landscape suggests a need for intersectoral networks to overcome silos and structural barriers in practice. This is consistent with population health approaches that value intersectoral action and upstream approaches, yet face implementation, scale, and continuity barriers. Recognizing this, the 2023 Centre for Healthy Communities Innovation Forum brought together over 130 multi-sectoral partners and representatives of organizations working on a municipal community safety and well-being policy or related social innovations. This paper reports on the topics, challenges, and calls for action that emerged from this event. Two calls to action emerged: (1) safety and well-being should be a central concern and investment priority for all levels of government; and (2) collaborative action across sectors and at different decision-making levels are critical for policy positive outcomes from community safety and well-being policies over the near and long terms. Intersectoral action must take a systems approach, connecting various levels to identify tangible actions and achieve transformative, population-level impacts on public safety and well-being, social outcomes, and economic vitality goals within communities. There is a pressing need to foster a funded system (or network of systems) to support the intersectoral coordination and collaboration required for ongoing cultivation and maintenance of an effective municipal community safety and well-being policy.
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 | 0.057 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".