Comparative Analysis of Lexical Bundles in Dissertation Abstracts: Insights for Teaching Academic English to Chinese Students
Bibliographic record
Abstract
Lexical bundle research in academic abstracts has predominantly focused on research articles, with less attention given to dissertation abstracts. This is particularly relevant for Chinese graduate students who are required to provide English abstracts in their dissertations. Addressing this gap, the study compared the structural and functional distribution of lexical bundles in dissertation abstracts by linguistics students from China and the United States to inform academic instruction. Two corpora, the Chinese University Student Collection and the American University Student Collection, each with 700 abstracts, were compiled and analyzed. The findings showed that Chinese students proportionally used more noun phrase (NP) and prepositional phrase (PP)-based lexical bundles, but fewer verb phrase (VP)-based ones, compared to their American counterparts. Additionally, they used a higher proportion of research- and participant-oriented bundles, but fewer text-oriented bundles. These differences highlight distinct structural and functional preferences in lexical bundle usage between the two student groups. This study underscores the importance of adapting instructional strategies to address these differences, enhancing English academic writing skills of Chinese graduate students by acknowledging the diverse linguistic approaches of international student populations.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".