Addressing Complex Societal Problems: Enabling Multiple Dimensions of Proximity to Sustain Partnerships for Collective Impact in Quebec
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sustainable solutions for complex societal problems, like poverty, require informing stakeholders about progress and changes needed as they collaborate. Yet, inter-organizational collaboration researchers highlight monumental challenges in measuring seemingly intangible factors during collective impact processes. We grapple with the question: How can decision-makers coherently conceptualize and measure seemingly intangible factors to sustain partnerships for the emergence of collective impact? We conducted an inductive process case study to address this question, analyzing data from documents, observations, and interviews of 24 philanthropy leaders and multiple stakeholders in a decades-long partnership involving Canada’s largest private family foundation, government and community networks, and during which a “collective impact project” emerged in Quebec Province, Canada. The multidimensional proximity framework provided an analytical lens. During the first phase of the partnership studied, there was a lack of baseline measurement of largely qualitative factors—conceptualized as cognitive, social, and institutional proximity between stakeholders—which evaluations suggested were important for explaining which community networks successfully brought about desired outcomes. Non-measurement of these factors was a problem in providing evidence for sustained engagement of stakeholders, such as government and local businesses. We develop a multidimensional proximity model that coherently conceptualizes qualitative proximity factors, for measuring their change over time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it