Participatory Governance as a Success Factor in Equity Crowdfunding Campaigns for Cultural Heritage
Bibliographic record
Abstract
This study seeks to address a research gap about the role of participatory governance as a success factor in successful equity crowdfunding (ECF) campaigns in the cultural heritage sector. The research stems from calls coming from both equity crowdfunding and cultural heritage research. Concerning equity crowdfunding research, academics have pointed out the need for more research on specific economic sectors and topics related to governance. Concerning cultural heritage and equity crowdfunding, our investigation is in line both with the calls for differentiation of funding schemes that could increase the financial resilience of cultural heritage organizations and with the academic and policy debate on the need to promote engagement and participation, also through participatory governance. Via QCA (Qualitative Comparative Analysis), this research investigates the peculiarities and success factors of equity crowdfunding for cultural heritage, with a special focus on participatory governance. The results indicate that ECF campaigns in this field can raise more funds than the targeted ones if they propose participatory governance schemes and enhance emotional and cultural heritage-related signals, thus differentiating ECF in cultural heritage from ECF in other sectors.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".