The evaluation of social innovation: A review and integration of the current empirical knowledge base
Bibliographic record
Abstract
Social innovation has gained prominence as a way to address social problems and needs. Evaluators and social innovators are conceptualizing and implementing evaluation approaches for social innovation contexts; however, no systematic effort has yet been made to explore and assess the overlap between evaluation and social innovation based on the empirical knowledge base. We address this gap, drawing on 28 empirical studies of evaluation in social innovation contexts to describe what evaluation practices look like, what drives those practices, and how they affect social innovations. Findings indicate most had developmental purposes, emphasized collaborative approaches, and used multiple methods. Prominent drivers were a complexity perspective, a learning-oriented focus, and the need for responsiveness. Reported influences on social innovations included advancing strategies, improving delivery, balancing aggregate and local information needs, and reducing risk. Conflict resolution, the quality of relationships, and availability of time and capacity mediated these influences. More peer-reviewed empirical studies and a broader range of study designs are needed, including research on how evaluations influence social innovation processes over time, phases, space and scale.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".