Bibliographic record
Abstract
Equity crowdfunding (ECF) is quickly emerging as an important mechanism to help bridge a major funding gap for small businesses. ECF provides an opportunity for these businesses to leverage technological advances that provide access to funds from a wider range of potential investors, including those retail investors who may previously not have participated in investing. This report on ECF was prepared for the Association of Southeast Asian Nations (ASEAN). The objectives of the report are to provide information and recommendations for member states in ASEAN which have not yet introduced ECF on the key issues they should consider in designing a regulatory framework for ECF; and for those ASEAN member states which have introduced a regulatory framework for ECF, provide information on developments in the regulation of ECF in other jurisdictions. This report: (1) defines ECF; (2) compares ECF to other forms of crowdfunding; (3) outlines the roles of the main participants in the ECF process (issuers, investors, and platform operators); (4) discusses the economic background to ECF including the main justifications for the introduction of ECF into ASEAN; (5) identifies the benefits and risks associated with ECF; and (6) provides detailed analysis of ECF in four jurisdictions – two ASEAN member states (Malaysia and Thailand) and two jurisdictions which are not members of ASEAN (the United Kingdom and Australia) together with briefer discussion of developments in ECF regulation in the United States, New Zealand, Canada, China, Hong Kong, and Singapore. This analysis of how ECF is regulated in a number of jurisdictions leads to a series of recommendations regarding the key issues that should be considered in designing a regulatory framework for ECF.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".