A Meso-Theory of Impact Evaluation: How and When Social Enterprises Use Impact Evaluation
Bibliographic record
Abstract
Impact evaluation is used by mission-driven organizations to not only identify but also to create impact. In this article, we explore how and when social enterprises use impact evaluation to create social impact. We find impact evaluation adds value by supporting social enterprises to set goals, innovate, scale and expand scope, and when mission is aligned with mandate. Mission reflects the internal impact goals of an organization and mandate is externally imposed by powerful resource providers including funders and investors. Building on these findings and resource dependency theory, we theorize a recursive process of impact evaluation where alignment between mission and mandate allows impact evaluation to be tightly coupled with an organization’s impact creation activities but as an organization develops, its mission and mandate can become misaligned, requiring it to also direct impact evaluation toward managing resource dependencies. We theorize two different outcomes: mission drift and mission perseverance. Taken together, our findings and theoretical model contribute to a more dynamic and strategic understanding of impact evaluation that accounts for its multiple uses and the interactions between them.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".