Social Impact Measurement: A Systematic Literature Review and Future Research Directions
Bibliographic record
Abstract
This paper explores the current state of the social impact measurement (SIM) field to better understand common practices in measuring the post-intervention social impact of a program or project and to identify strategies to improve measurement in practice. This study employed a systematic literature review. Articles were manually coded deductively and inductively in NVivo to complete a descriptive and thematic analysis of the literature. The thematic analysis provided an in-depth understanding of the SIM field. We found that similarities existed across the definitions of social impact (e.g., environmental impact is part of social impact). Additionally, social return on investment (SROI) is the most common measurement model and theory of change was identified as a core concept across SIM literature. Strategies are presented for practitioners to consider when measuring social impact, including: (i) engage stakeholders throughout the process, (ii) mobilize existing operational data, (iii) increase measurement capacity, and (iv) use both qualitative and quantitative data. This study reveals the nuances of SIM based on academic literature published across the globe over the span of a decade. It places emphasis on the post-intervention stage and identifies strategies to improve the application of measurement models in practice. Lastly, it outlines future research directions.
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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 | 0.155 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".