Cognitive Analysis of the “Discourse Stances” in English News Reports on Smog in China and America
Bibliographic record
Abstract
This analysis contrasts on Chinese smog news (CSN) with American smog news (ASN), probing into the complicated discourse stances and their represented cognitive mechanism. Having been assisted by “glossary extraction”, the authors uncover the correlation between varied stance glossaries and the hidden cognitive mechanisms. The research provides hints on social cognition in news encryption and decryption, based on the database of thirty pieces of news reports from Chinese news agencies and thirty from American sources respectively. The analytical results reveal that Chinese news frequently quotes the comments of officials and is largely dominated by official and political stances of government, whilst American news frequently features occupational and public stances with pervasive individual and personal tones, attitudes, and dictations. This cognitive research on English weather news reports casts light on the discrepancies and commonalities in the adoption of stance glossaries in media discourse, drawing respective cognition construction of media writers from different cultures, which further illustrates how public cognition being framed on social issues in discourses.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".