A Study of Interactional Metadiscourse in English Abstracts of Chinese Economics Research Articles
Bibliographic record
Abstract
This study adopts the revised interpersonal model of metadiscourse to discover whether and to what extent Chinese authors employ a varying amount of Interactional Metadiscourse (IM) in the past decade in English abstracts of economics Research Articles (RAs). The data was drawn from a prestigious economics journal in China to compose a corpus of 289 abstracts. The analysis indicates that Chinese authors harmonize with English counterparts by capitalizing on more hedges, while the amount of boosters unexpectedly maintains at a high level over time, both of which can be attributed to the interaction of Chinese deep-seated cultural leanings and Anglo-American cultural preferences. Consistent with our prediction, attitude markers exhibit no significant difference along the years given the initial parallel with English counterparts. However, there are significant differences of self-mentions in the past decade regardless of English rhetorical conventions, which may be attributed to economical influences in China. With regards to engagement features, they remain relatively underused with no marked difference within the period resulting from the genre-specific factor which confines their use.
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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".