A Corpus-based Study on the Use of Three-word Lexical Bundles in the Academic Writing by Native English and Turkish Non-native Writers
Bibliographic record
Abstract
The utilization of English recurrent word combinations –lexical bundles- play a fundamental role in academic prose (Karabacak & Qin, 2013). There has been highly limited research about comparing Turkish non-native and native English writers’ use of lexical bundles in academic prose in terms of frequency, structure and functions of lexical bundles (Bal, 2010; Karabacak & Qin, 2013, Öztürk, 2014). Therefore, this current research was conducted in order to investigate the most frequently used lexical bundles in the academically published articles of Turkish non-native and native speakers of English and to investigate whether there was a significant difference between native and non-native scholars with respect to the frequency, structures and functions of English language lexical bundles. The data were collected from two corpora; 15 scientific articles of native speakers and 15 scientific articles of Turkish advanced writers. The investigation included a quantitative analysis of the use of three-word lexical bundles and a qualitative analysis of the functions and structures they serve. To be more conservative, three-word lexical bundles which occur 40 times per million words and appear in 5 different texts were described a lexical bundle in this current research. The findings revealed that Turkish non-native writers showed underuse and less variation in the use of lexical bundles in their academic prose compared to native speakers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".