Challenges of impact measurement in an emergent social economy
Bibliographic record
Abstract
The social impact measurement theory and practice is an early emergent field in Romania, despite all the recent significant advances and of the maturation of the topic at the international level. However, even if the size and dynamics of the social economy sector are not yet comparable with European countries with tradition in the sector, Romania faces a trend of discovery, re-discovery, and development of the social economy, present in a diversity of organizations and fields and models of classic or highly innovative social enterprises. Advancing social impact measurement in Romania becomes imperative for public authorities and also the whole society to understand how much positive social change can be attributed to the social economy organizations. The main objective of this paper is to test the effectiveness of the impact indicators proposed by the Ministry of Labor and Social Justice (MLSJ) in the indicative guidelines, which were elaborated after the adoption of the Methodological Norms for applying the Law of the Social Economy by Government Decision no. 585, on 10 August 2016, and which represents the first official regulatory attempt of impact measurement. Applicative research will be carried out in two social economy organizations active in the social services field (Heart of Child Foundation from Galati county, and Charitable Foundation Sf. Daniel from Cluj county, Romania) for analysing the current metrics used in measuring the social impact in the last three years (2017 – 2019), and the relevance of the indicators proposed in the indicative ministerial framework for their organizations. After reviewing various international approaches and frameworks of impact measuring, testing the indicative impact indicators proposed by the MLSJ, and having in-depth interviews with the managers of the analysed social enterprises, the article concludes with a set of recommendations for the development of a more effective impact measurement framework.
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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 | 0.120 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| 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".