Cohesive Ties in Scientific Texts: An Analytical Approach
Bibliographic record
Abstract
The notion of “textuality” encouraged Halliday and Hasan in 1976 to present their model of discourse analysis through raising questions about whether “cohesion” is a semantic concept or a structural relation, whether a text is a structural unit or not or even if there are semantic or structural relationships within a text. Cohesion is like the glue that unifies the meaning within a text through binding the textual elements. Several studies applied the model of Halliday and Hasan on different texts such as legal, political, narrative, etc., but, very scarce attention has been given to scientific texts. The aim of this study is to examine and analyze some medical texts chosen randomly in terms of the Halliday and Hasan’s model by identifying both the lexical and the grammatical cohesive ties. The data analysis shows that the grammatical cohesive ties of reference and the lexical cohesive ties of reiteration carry the highest frequency among other cohesive ties. These results confirmed the significant role of cohesive ties in scientific 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".