A Textual Discourse Analysis of Introductions in Textbooks of Humanities and Basic Sciences
Bibliographic record
Abstract
This study attempted to scrutinize the disciplinary and cross-disciplinary generic variations of textbook introductions in humanities and basic sciences with reference to the onion model. The data included a sample of 60 introductions (i.e. ten each from applied linguistics, sociology, and psychology within humanities, and ten each from biology, agriculture, and geology within basic sciences). Humphrey and Economou’s (2015) onion model of discourse analysis was adopted, and MAXQDA 10 software of qualitative analysis was used for coding. The findings revealed similarities and differences not only within but also between the two disciplines, which can be attributed to the nature of disciplines as well as the generically accepted conventions in each discipline. Moreover, the results of the study highlighted the predominance of descriptive and analytical phases in textbook introductions not only in isolation but also in establishing more complicated genres like persuasion and critique. The study highlights the importance of teaching which is concerned with learners' rhetorical consciousness-raising of the disciplines as well as the discipline-specific lexico-grammatical features in expert texts.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".