The Effects of Large-Scale Social Movements on Language Attitudes: Cantonese and Mandarin in Hong Kong
Bibliographic record
Abstract
Speakers with standard accents are typically judged more favorably than non-standard speakers, but this may shift in response to perceived intergroup conflict with ethnolinguistic outgroups. Three studies were conducted to examine how large-scale social movements may impact language attitudes in Hong Kong. Attitudes toward standard-accented and non-standard-accented Cantonese and Mandarin were collected across four instances in 2013 and 2015 (pre- and post-Umbrella Movement), 2018 and 2019 (pre- and post-Anti-Extradition Bill Movement), respectively. Compared to Study 1 (2013), Hong Kong participants judged standard speakers of Cantonese (the ingroup variety), and ingroup, non-standard speakers of Mandarin (the outgroup variety) significantly more favorably in Study 2 (2015). Study 3 showed that the retrospective endorsement of the Umbrella Movement moderated preferences for standard Cantonese and Mandarin speakers. Comparison of 2018 and 2019 data partially replicated the findings in Studies 1 and 2, though the current endorsement of the Anti-Extradition Bill Movement did not moderate preferences for standard speakers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".