A Corpus-based Study on the Use of Three-word Lexical Bundles in the Academic Writing by Native English and Turkish Non-native Writers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it