Reading between the lines: Legal risk mitigation by equity crowdfunding platforms
Bibliographic record
Abstract
The use of equity crowdfunding as a source of financing has rapidly gained traction. The key motivation of funders on such equity crowdfunding platforms (ECPs) is high financial returns, which is also associated with greater risk for all stakeholders: creators, funders and ECPs. We explore legal risk mitigation by ECPs in this paper and develop a taxonomy of legally mitigated risks. Content analysis of Terms of Service and Privacy Policy contracts of 17 most popular ECPs each, results in 544 references of legally mitigated risks and a taxonomy of 12 first level items and 18 second level items. We find that platforms fear and mitigate for risks associated with Information Security and Third Party most. The importance attached to Third Party risks is especially interesting. This theory and experience based systematic and comprehensive taxonomy of legally mitigated risks would not only help users understand a rapidly evolving phenomenon but also will help regulators monitor compliance issues.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".