Financiación de las entidades de Economía Social: de los modelos tradicionales a los mecanismos de financiación innovadores.
Bibliographic record
Abstract
The centrality of the social mission in social entities has implications for both the economic and funding relations.As a type of organisation deeply rooted in achieving a social objective, these organisations access a multitude of financial resources.Social economy organisations need to acquire and combine the necessary resources and manage relationships with external resource providers to achieve their social mission.Social economy entities finance is nowadays more complex and articulated than years ago.The landscape and the resources available have changed and social enterprises are transformed and raise financing from many new sources.Venture philanthropy, Impact investing, Crowdfunding and Result-based Financing (RBK) are focused on supporting social-based organisations and creating social impact.It is more than the provision of financial resources.Instead, it is a discourse around how to offer finance by introducing new modalities and a new culture around intermediation.In this realm, social impact and its measurement are key issues.This paper aims to describe the available innovation in funding for social economy entities and how the financing needs of social economy entities have become more complex, requiring therefore new mechanisms.Thus, this piece of research applies a qualitative methodology by reviewing the academic and practitioner literature on the topic.The result is an overview of the current and innovative funding initiatives for social organisations.Furthermore, this paper adds in the debate on social economy entities important elements about the demand and the current supply of financial resources.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".