A corpus-assisted analysis of language convergence and meaning divergence of ‘mental health’ in Asian countries
Bibliographic record
Abstract
There has been an increased use of the term ‘ mental health’ to refer to more negative states or ‘mental illness’ . This study examines the language and meanings associated with ‘ mental health’ in English-language newspapers across five Asian countries: Thailand, Malaysia, Singapore, the Philippines, and China. The aim is to identify the common words used to discuss mental health and to assess the extent to which these words reflect their common definitional meanings across different cultural contexts. Methodologically, the study integrates language convergence and meaning divergence approaches with corpus linguistics to analyse the newspapers. The findings reveal that ‘ mental health’ is frequently collocated with words such as ‘ issues’ , ‘ problems’ , ‘ services’ , ‘ support’ , ‘ physical’ , and ‘ people’ across the Asian news corpora. It is found that these collocates often diverge from their definitional meanings and are often used in reference to more negative mental states across the Asian news corpora.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".