Building collective efficacy and sustainability into a community collaborative: Community solution to gang violence
Bibliographic record
Abstract
In existence for over 6 years, the Community Solution to Gang Violence (CSGV) is a community-based initiative in Edmonton, Alberta, Canada that includes over 30 organizations working together on a strategic approach to prevent youth gang involvement. As a follow-up to an earlier article, this paper explores the viability of CSGV's future by assessing its collective efficacy and issues relating to sustainability by drawing on documents and records produced by the CSGV project manager and interviews with working group members. CSGV members continue to express a commitment to the initiative's objectives, but the scope of the collaborative has reached a point where change may be necessary in order to access the kinds of funds necessary to sustain its momentum. Using the four pillars of the collaborative, knowledge translation, engaged network, community awareness and support from community leaders and funders, this paper provides a look at the collective efficacy of CSGV including a discussion of some of its successes and the challenges it faces, through the insights of a sample of its members. The paper concludes with speculation on the sustainability of the collaborative.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".