Discourse Patterns in Selected Science-Based Postgraduate Theses’ Abstracts
Bibliographic record
Abstract
The paper identified and analysed patterns of discourse thematic progression (TP) in Science-Based postgraduate theses abstracts in selected universities in Southwestern Nigeria. It described the linguistic realization of TPs across the identified four motifs of text such as introduction, methodology, findings and conclusion and also discussed the content of texts via the functional categories of texts. One hundred and fifty PhD theses abstracts out of six hundred and three abstracts produced in the Sciences of the Obafemi Awolowo University, Ile-Ife, University of Ibadan and University of Lagos were selected through simple random sampling. An analysis of progression of theme and rheme was done on the selected abstracts by adopting the framework of the functional sentence perspective as propounded by Dane’s (1970 and 1974) and the systemic linguistic approach. The theme-rheme analysis of texts and their linguistic realization across the identified motifs was done to unravel the content of the abstracts. The findings showed that all TP patterns (Constant TP, Simple Linear TP, Derived TP and Split Rheme TP) featured in the text. The Constant TP predominates the text motifs of texts. These linguistics features largely associated with the Constant TP were the nominal phrases, noun, pronoun, cleft among others. The study concluded that text content, thematic patterns and linguistic features integrate to create meaning in text and one way to understand texts better is to unravel their patterns in 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.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".