Enabling evidence-led collaborative systems-change efforts: an adaptation of the collective impact approach
Bibliographic record
Abstract
Abstract This article conveys the results of a three-year ethnographic study of a pan-Canadian community–university collaboration to prevent and end youth homelessness. The collaboration adapted aspects of a collective impact (CI) approach to pursue a large-scale shift in how youth homelessness is addressed in Canada. The objective of this article is to codify and share the model developed and implemented by the community–university collaboration as an opportunity for ongoing adaptation and learning among others undertaking similarly complex and collaborative systems-change efforts. Findings suggest a CI approach is unlikely to be suitable for large-scale innovation-oriented initiatives, and that context-specific adaptations of the model should be encouraged. To what is already known about collaborative multisectoral partnerships, this article reveals the importance of strategic information sharing, targeted and flexible research and knowledge mobilization efforts, and ongoing attentiveness to the relational dimensions of collaborative evidence-informed systems-change efforts.
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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 | 0.096 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.019 | 0.038 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".