Bibliographic record
Abstract
Abstract This paper reports the first case in which a linguist served as an expert witness in Hong Kong, a former British colony that has operated as a special administrative region of the People’s Republic of China (PRC) since 1997. The dispute was on the meaning of the political slogan “Liberate Hong Kong, Revolution of Our Times”, which was widely adopted during the 2019–2020 protests. The keywords “liberate” and “revolution” are smoking gun evidence for the prosecution in a large cluster of cases that involve sedition law and national security offences. Section I of the paper provides background information about a case the author was involved in, which was concerned with whether the slogan was seditious. Section II describes the analysis conducted, which concludes that the slogan as a whole refers to a need to rectify a problem and to return to the original, a more desirable state of affairs for Hong Kong, without specifying what problem there is and what the desirable state of affairs looks like. Section III highlights some critical issues in the analysis, discussing challenges faced and ethical questions for the expert witness. Section IV is a postscript that briefly describes the outcome of the case.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".