Social Value as a Mechanism for Linking Public Administrators with Society: Identifying the Meaning, Forms and Process of Social Value Creation
Bibliographic record
Abstract
Despite public/private/third sector organisations creating and demonstrating the social value of their interventions, they face challenges in understanding and executing the process of social value creation, due to both the lack of definition and theoretically embedded explanatory models of social value. This article seeks to fill this gap by defining social value and identifying the process involved in social value creation from a Weberian standpoint of social action, class and power. Nine resource capitals were identified that contribute to the creation of social value in society: social, ethical, cultural, human/intellectual, physical, economic/financial, environmental/natural, religious, and political. The research utilises Q-methodology to develop a typology of social value and semi-structured interviews to understand the process of social value creation. The results reveal four-types of social value: action-driven, outcomes-driven, sustainability-driven and pluralism-driven, which can be derived through individual/collaborative and resource capitals-driven processes. An integrated framework for social value creation, embedded within a Weberian theoretical framework, is presented to assist policy-makers to commission social value, and public/private/third sector organisations to deliver social value.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.063 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".