Key factors for success of social enterprises in Italy: analysis if the financial and operating performance
Bibliographic record
Abstract
Assessing social performance is one of the greatest challenges for practitioners and researchers in social entrepreneurship. Even though social enterprises (SEs) have the main goal of achieving social purposes, they should also be able to economically and financially survive to meet their aim and accomplish their tasks. To this purpose, we investigate if the key factors leading to the financial and operating performance are the same as those of for-profit firms, by using Italian data at a firm level during the period 2002-2013. We find that the standard financial and operating factors characterising for-profit firmsi¯ performance play a crucial role for SEsi¯ results as well. Moreover, territorial and socio-economic variables seem to have a positive impact on financial performance. From a policy perspective, this may imply that further programs (e.g. safety-oriented and those promoting facilities in the territory) should be locally adopted to support the SEsi¯ activity and development.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".