Bibliographic record
Abstract
Event Description ‘Investment crowdfunding’ is a new and inclusive form of online venture capital market open to all investors, both retail and accredited. It’s like Kickstarter, except the backer gets a share of stock, which would be an illegal public offering of unregistered securities—absent the special exemption adopted as part of the federal JOBS Act of 2012. Since then, many other jurisdictions—including Canada, Australia and the EU—have enacted analogous legal regimes, each a bit different than the others. Schwartz recently published the definitive guide to investment crowdfunding, based on ten years of on-the-ground research, including as a Fulbright Scholar in New Zealand. He will address the law and practice of investment crowdfunding in the United States, and compare it with other jurisdictions. Speaker Bio Andrew A. Schwartz, Professor of Law at the University of Colorado, is a leading international scholar in the field of investment crowdfunding, and the author of a new book on the subject, Investment Crowdfunding (Oxford University Press). Schwartz earned an engineering degree from Brown University and a law degree from Columbia University, then clerked for two federal judges and practiced corporate law at Wachtell, Lipton, Rosen & Katz in New York. He entered academia in 2008, when he joined the law faculty of the University of Colorado. He teaches and publishes on corporate, securities, and contract law, and his many articles have appeared in leading journals including the UCLA Law Review and the Yale Journal on Regulation. In 2017, he served as a Fulbright Scholar in New Zealand, where he studied investment crowdfunding
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.399 | 0.181 |
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".