The strategic ritual of emotionality in Chinese and Australian hard news: a corpus-based study
Bibliographic record
Abstract
This article, based on the appraisal framework, investigates the ways in which Chinese and Australian journalists strategically mobilize and mediate emotions in hard news reporting on risk events that disturb social order. Drawing on a newly built comparable corpus of Chinese and Australian hard news reporting on risk events, the study found that both Chinese and Australian journalists endeavour to reconstruct social order in the face of risk events mainly through building a shared feeling community. However, Chinese and Australian journalists strategically communicate emotions to construct different centres of social values. In Australian hard news, the centre of social values holding the nation together is construed through ordinary citizens, whereas in the Chinese context the centre is construed through power elites. The article argues that such different strategic rituals of emotionality are conditioned by the press conditions (e.g. tightening media budget, increasing press competition, and rising broadloidization), and that they reflect divergent stances undertaken by Chinese and Australian journalists.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".