China’s Stance on Rohingya Refugees Issues in The Local Newspaper Through Corpus Sentiment Classification
Bibliographic record
Abstract
The predicament of the Rohingya refugees has garnered significant attention in China. The present study scrutinises the newspaper's language alignment to the representation of China’s stance on the Rohingya refugees. The primary objectives are twofold: firstly, to analyse the top ten salient nouns, verbs, and adjectives, and secondly, to evaluate the sentimentality of the aforementioned salient vocabulary and phrases. The present study employs a mixed-method approach with a corpus-driven research design. The corpus was procured from China Daily, comprising a total of 78 newspaper reports with 24,769 words and 1,013 sentences. The utilisation of Sketchengine and Atlas.ti, was selected. A wordlist of vocabulary has been produced for top ten salient nouns, verbs, and adjectives. The vocabulary was compared with the British National Corpus for keywords. Subsequently, the keywords were employed for trigrams and qualgrams. The presented materials consisted of concordances pertaining to phrases. The results were analysed sentimentally. The top ten salient nouns, verbs, and adjectives included Myanmar, people, Rohingya, be, have, say, more, human, and international. Then, keywords were compared to the reference corpus to select significant trigrams and qualgrams produced by Myanmar, Rohingya, migrants, Bangladesh and humanitarian. Sentimental analysis was performed on 60 linguistics items. Referring to the findings, the nation of China had a neutral stance. The findings suggest that scholars and politicians may benefit from a more empirical approach to analysing a nation’s stance, as opposed to relying solely on subjective interviews, as reported language can serve as a factual basis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".