Genre Analysis of Research Article Abstracts in Linguistics and Literature: A Cross Disciplinary Study
Bibliographic record
Abstract
The importance of an abstract in a research article has turned the focus of linguistics on Genre analysis of abstract articles. Taking into consideration this immensely researched topic, this paper aims to investigate the macro and micro structures in the Linguistics and Literature Abstracts. In the previous researches, this very comparison is never addressed by the researches, hence the present research aims to fill this gap. The corpus contained 40 abstracts, 20 of linguistics and 20 of literature from International Journal of Applied Linguistics & English Literature (IJALEL). The macro analysis was made according to the Create a Research Space (CARS) model by Swales (2004) and Ant mover software was used to analyze the corpus, while the micro analysis followed Swales and Feak (2009). The results showed that there is no significant difference between the linguistics and literature abstracts at the macro level while the differences lie at the micro level. This study will be beneficial for the novice researchers as it provides a framework of analyzing two interconnected disciplines.
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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".