Toward a systems approach to social impact assessment
Bibliographic record
Abstract
Purpose The purpose of the paper is to propose a systems change lens to current approaches to assessing social impact in social ventures. Many existing tools for measuring social impact are limited in their capacity to assess the inherent complexities and interconnected nature of the work done by social enterprises. Design/methodology/approach The paper uses in-depth interviews with sector experts to gain insights into their needs related to impact assessment, as well as issues they face when attempting to understand and measure their impact. Findings Expert interviews provide insights into how social impact occurs through interconnected systems. It also highlights the need for impact assessment to better consider interaction within systems and networks. Results support previous work concerning the need for methods that can better account for complexity, interacting problems and the place of power in influencing actions. Research limitations/implications Following results from interviews and review of existing literature, symbolic interactionism and Social Worlds/Arenas theories are used to gain insight as to how impact can be conceptualized in terms of systemic shifts in social equilibria. The model proposes to capture the contested definitions of problems and their negotiation in social structures. Originality/value Grounded in sociological theory, the model brings a new theoretical approach to social impact assessment, one that provides a different view of social structures than existing models that are grounded in economic metrics. The proposed model, therefore, provides a new lens for the detailed assessment of the complex interactions between systems.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".