Anita Anand, "Is Crowdfunding Bad for Investors?” (2014) 55:2 Canadian Business Law Journal
Bibliographic record
Abstract
With the passing of the Jwnpstart Our Business Startups ( JOBS) Act, U.S. companies and their investors will soon be able to participate in "equity crowdf unding" ( ECF), a process that allows individuals to buy equity securities in a company over the Internet.After assessing arguments both for and against ECF, this article concludes that the benefits of ECF, on the whole, outweigh its disadvantages.ECF provides small and mid-size companies with an economical system for raising capital, decreases costs incurred to invest in these companies, offers investment opportunities to a greater population, and pairs companies with intetested investots.The article makes some proposals for the regulation of ECF, such as requiring the distribution of securities to occur through portals that are registered with the securities regulator and it briefly addresses the Ontario Securities Comm.ission's recently proposed crowdfunding prospectus exemption. I. INTRODUCTIONA trend known as "equity crowdfunding'' (ECF) is becoming popular as a means by which firms and individuals raise capital by selling securities to investors over the Internet.Some argue that this novel type of financing, endorsed and perhaps spawned by the Jumpstart Our Business Startups (JOBS) Act, 1 is contrary to investor interests because the benefits of the proposed regulation do not outweigh the costs.2 Others contend that ECF advances the interests of investors, and of retail investors especially, by increasing the nmnber of investment opportunities available to them. 3 ls it possible for a particular policy proposal to be simultaneously good and bad for investors?"' I.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.024 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".