A Textual Discourse Analysis of Introductions in Textbooks of Humanities and Basic Sciences
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it