Thematic Progression in Economic Discourse: A Case Study of the English-Chinese Reports from The Economist
Bibliographic record
Abstract
The patterns of thematic progression, greatly influenced by culture-peculiar thinking patterns and language-particular features, reflect the integration of form and meaning in the flow of information in discourse. The economic discourse has its distinct linguistic characteristics and important communicative purposes. Thus, related research on the thematic progression of economic discourse is important for us to understand the language use in the context of economic and financial activities and also has important implications for language learning and teaching, translation, automated language information processing etc. This study first employs CiteSpace, a document visualization tool, to review the existing related studies on the economic discourse in China, and then analyzes the major patterns of thematic progression in the economic discourse based on the English and Chinese reports from The Economist. Through our discussion, we aim to explore universals and peculiarities of thematic progression in English-Chinese economic discourse and discuss the reasons attributing to major distinctions between the two languages in terms of thematic progression.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".