Corpus-Based Comparative Analysis of Quantitative and Qualitative Research Articles in TEFL: A Lexico-Grammatical and Move Structure Approach
Bibliographic record
Abstract
This study examines the abstract, introduction, results/discussion, and conclusion sections of quantitative and qualitative academic papers in the field of TEFL, focusing on their lexico-grammatical and move-structure features. Utilizing both quantitative and qualitative methods, the research explores potential differences between these two genre-specific corpora in terms of their linguistic and rhetorical characteristics. The analysis of move-structure was based on Swales’ CARS model (2004). A mixed approach of computer-assisted and manual analysis was used to ensure validity. Fifty research articles from ELT journals, representing both quantitative and qualitative approaches, were selected for analysis. Statistical interpretation of the results, including vocabulary profiles, readability statistics, and move-step structures, was conducted using the non-parametric Mann-Whitney U test. The results, with a significance level of P < 0.05, indicated that most lexico-grammatical features across abstracts, introductions, results/discussion, and conclusions in quantitative and qualitative papers were not significantly different. However, move-structure analysis revealed distinct variations between the two genres across these sections. These findings provide valuable insights for academic researchers in the EFL context, suggesting that while research methodology is important, the choice of topic and the researcher’s unique perspective may be more critical in shaping the study.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".