Bibliographic record
Abstract
Language and society are closely interrelated as every change in the society is reflected in our language. Critical discourse analysis (CDA) is an important tool for our understanding of the society. We made a CDA analysis on the announcements Liu made in her prosecution against Li, a famous anchor in China, following a sociological investigation approach proposed by Fairclough in his Language and Power, and cognitive method as a supplement, trying to find out what is buried under the language. Key words: CDA, social ethics, narrative, ideology, profit-loss scheme Resume: Il existe une relation inseparable entre la langue et la societe et chaque changement social se reflete dans la langue. En tant qu’un outil puissant d’analyse, l’analyse critique du discours joue un role tres important dans la connaissance de la societe. Nons effectuons, en employant la methode d’analyse sociale preconisee par Fairclough et la methode d’analyse cognitive, l’analyse critique du discours du proces de Liu intente contre Li. Avec le corpus de quatres declarations de Liu Yin, nons s’efforcons d’explorer la conscience cachee sous la langue superficielle. Mots-cles: analyse critique du discours, morale sociale, narration, ideologie sociale, logique de benefice et de perte 摘要：語言與社會之間具有密不可分的關係，社會的每一個變化都能在語言中得到體現。批評話語分析作為一項有力的分析工具對於認識社會具有非常重要的作用。我們使用 Fairclough所宣導的社會分析方法結合認知分析方法對劉某訴李某案進行了批評話語分析，語料為劉穎所作的四次公開聲明，盡力挖掘其表面語言背後所隱藏的意識。 關鍵詞：批評話語分析；社會道德；敍事；社會意識形態；賺賠邏輯
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".