Financing local development through crowdfunding: An empirical analysis of social projects in Portugal
Bibliographic record
Abstract
Crowdfunding (CF) is an increasingly attractive source to fund social projects. However, to our best knowledge, the study of CF for social purposes has remained largely unexplored in the literature. This research envisages a detailed examination of the role of CF on the early-stage of the social projects at regional level. By comparing the characteristics of the projects available in the Portuguese Social Stock Exchange (PSSE) platform with others that did not use this source of financial support, we explore its role on regional development. The results we got show that, in most cases, both PSSE and Non-Governmental Organizations projects complemented the services offered by the State or by the private sector. Furthermore, about a quarter of the projects present in PSSE operated in areas that were not being addressed neither by the services offered by the State nor by the ones of the private sector. The results attained show that more recent social ventures have a greater propensity to use PSSE. The same is find in those organizations which work closely with the target audience. We also observed that the use of PSSE was correlated with the geographical scope of the Social Venture. The circumstance of having the social organization acting at a local or regional level seems to be strongly associated with the possibility of using social crowdfunding for financing social projects.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".