https://www.puntoorginternationaljournal.org/index.php/PIJ/article/view/90
Bibliographic record
Abstract
Thanks to fundraising from small investors, principally through social media and online forums, equity crowdfunding (EC) is emerging as an important new financing mechanism for new ventures. However, the literature about equity crowdfunding and social media is still scarce, and no studies have jointly investigated these topics in Italy. As a result, this study aims at investigating, through an exploratory quantitative research approach based on social network analysis (SNA) methodology, the role that equity crowdfunding platforms have on social media, specifically on Twitter. The results of our study indicate that higher numbers of tweets and users spoke about equity crowdfunding following the introduction of Consob Regulation no. 20264 (17/01/2018) on equity crowdfunding and the growth of the use of this instrument in the first quarter of 2019. The study contributes to the literature on crowdfunding and social networks, shedding light on specific aspects typical of an equity model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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; both teacher heads agree on what is shown here.
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".