Bibliographic record
Abstract
This chapter focuses on the emergence of financial instruments and enabling environments for social enterprises in selected OECD countries, with particular focus on Western European countries, Canada and the United States, and possible strategies for supporting their development in Eastern European Countries. As social enterprises continue to draw the attention of national governments and local authorities alike in the fight against unemployment and social exclusion, they are also being embraced by civil society as a way of addressing unmet needs in a sustainable manner. Social enterprises are emerging in numerous sectors producing goods and services, thereby increasingly demonstrating their capacity as economic actors. They are similarly considered as key to socio-economic transformation in transitional economies. As the chapter suggests, the incompatibility of an existing investment framework, tied to outmoded and fixed categories that do not correspond to the new reality of social enterprises and their investment needs, requires cultural adaptation of the financial, legal, accounting and policy communities internationally to this new reality before the appropriate and enabling tools can be designed. For social finance to become sustainable finance, an integrated approach has to be adopted that is distinct from traditional capital markets. In conclusion, and regardless of the breadth of instruments available, the real potential of social enterprises will only be realised if they are integrated into a systemic approach to social exclusion, labour market transformation, and territorial (place-based) socio-economic development strategies that requires innovative public policy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".