Social impact measurement for the Social and Solidarity Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The OECD Global Action "Promoting Social and Solidarity Economy Ecosystems", funded by the European Union, through its work stream on social impact measurement, endeavours to: 1) explore current social impact measurement practices among social and solidarity economy organisations; 2) identify the methodologies best suited to capture the social benefits of the social and solidarity economy; and 3) understand what policy initiatives can be used to foster a social impact measurement culture and practice in the social and solidarity economy. After discussing the origins and drivers of social impact measurement, this paper examines existing methodologies developed at the local, national and international level and finally reviews how these are being implemented in the social and solidarity economy. It takes stock of the policy mapping exercise conducted by the OECD, which draws on responses to an online survey and on the stakeholder consultations conducted in Brazil, Canada, India, Korea, Mexico and the United States.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it