Bibliographic record
Abstract
Abstract Online misinformation endangers the infrastructure of fact essential to public discourse and presents an even greater threat where it is being utilised as a weapon by hostile state actors. In recognition of these dangers, Singapore has implemented legal measures to combat online misinformation, enacting in quick succession the Protection from Online Falsehoods and Manipulation Act (POFMA) and the Foreign Interference (Countermeasures) Act (FICA). These statutes open up novel frontiers of development for Singapore's free speech jurisprudence. Indeed, these statutes confer upon government authorities the power to compel the authors of certain material to display notices stating that the material contains falsehoods or originated from a hostile information campaign. Yet, should one accept that the constitutional right to freedom of speech extends to the freedom not to speak, the compulsion of such expressions may well be unconstitutional under Singapore's free speech guarantee. This article will study the theoretical justifications for a prohibition against compelled speech to evaluate whether Singapore free speech jurisprudence ought to recognise such a prohibition, propose a doctrinal framework to analyse compelled expressions by reference to US, UK, and Canadian jurisprudence, and critically assess how the POFMA and FICA would fare under such a doctrine.
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 | 0.011 | 0.017 |
| 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 | 0.026 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".