Bibliographic record
Abstract
This article seeks to understand the rationale for and potential implications of the introduction of dual class shares (DCS) in Singapore. It does so by first considering the theoretical as well as evidential arguments for and against the use of DCS, followed by a survey on the reception (or otherwise) of such structures in four common law jurisdictions with vibrant capital markets, viz., Canada, the United States, United Kingdom and Hong Kong. It observes that the chief argument cited by business founders to justify the use of DCS structures is the desire to enhance a firm’s long-term profitability by shielding the (talented) founder from short-term market pressures. Though the use of DCS structures remains controversial, the phenomenal success of technology unicorns such as Alphabet Inc. and Alibaba appears (for now) to have sealed the place of DCS in the American securities markets. This exerts considerable pressure on competing markets to follow suit. Singapore’s response to this aggressive competition is pragmatic but measured. The indications so far are that the regulators would chart a middle path between the conflicting goals of incentivizing entrepreneurial fundraising and investor protection by permitting DCS structures in exceptional cases circumscribed by stringent safeguards. This, it is submitted, is an appropriate response given the theoretical and evidential underpinnings of DCS structures as well as economic and regulatory conditions peculiar to Singapore. Should it succeed, this development would serve as an interesting and notable example of a regulatory innovation that avoids the proverbial race to the bottom in the face of intense competition.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".