Finding the Impact: Methods for Assessing the Contribution of Collective Impact to Systems and Population Change in a Multi‐Site Study
Bibliographic record
Abstract
Abstract John Kania and Mark Kramer put forward “Collective Impact” in 2011 as a framework for organizing multi‐sector collaborative efforts to achieve change at scale. The collective impact theory of change posits that by establishing and implementing its five conditions, groups can achieve meaningful systems changes to create long‐term gains in social and environmental conditions. While significant scale uptake has occurred, questions have remained about the degree to which collective impact, as an approach, actually works to achieve change at scale. In 2017, ORS Impact and Spark Policy Institute embarked on an evaluation effort to understand the degree to which the collective impact approach contributed to population‐level change across many sites. We sought to answer this question with as much rigor as possible, without attempting to simplify the complexity of the context, the variability of implementation of collective impact, or the many interim changes needed to see the impact at scale. This chapter shares the essential methods our research team used. We do not seek to share the findings; instead, we hope that others can learn from and use these methods to continue to strengthen the sector's understanding of when, how, and why different collaborative efforts work or do not. In addition to describing the key methods, the authors will reflect on considerations, lessons learned, and recommendations to other evaluators who might seek to answer similar questions or use similar tools and methods.
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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 | 0.183 | 0.303 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".