Public Policies and State and Market Failures Social Economy in the Context of Partnership with Civil Society Organizations
Bibliographic record
Abstract
The State as the Entity that represents the first sector of the economy, has the aim of being the provider and articulator of public policies so that the Market, as the second sector, produces utilities, generates employment and income, which are the bases for supporting economic and social policies. Failure to fulfill this mission produces losses, known as State failures, due to the lack of clarity and inclusive public policies. The market, dependent on economic policies, produces inefficiency, known as market failures. The article discusses the context of State and Market failures and partnerships with Civil Society Organizations from the perspective that partnerships can be one of the instruments to mitigate the consequences of the aforementioned failures, and aims to propose a quantitative-qualitative theoretical model to evaluate the contribution of the third sector in building the social economy, as well as evaluating the performance and sustainability of partnership projects, in the context of State and Market failures. Using academic data from the research group “Third Sector Research and Extension Laboratory - TSREL”, the model was tested and the results obtained suggest that the methodology is consistent in signaling that the project performance is efficient, effective and the partnership is sustainable, getting a robust contribution to the construction of the social economy, assisting regulators and public policy managers in monitoring the performance and sustainability of social policies.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".