English Morphemic Constituents Working for Discourse Wording: Extending Rank Scale from “Clause (Complex)” up to “Text (Type)”
Bibliographic record
Abstract
<p>This paper aims to elaborate Halliday’s observation of “text as wording” alongside the already accepted view of “text as meaning” and, accordingly, to address two interrelated issues: (i) how morphemic options work for such grammatical units as words, groups / phrases, clauses, clause complexes and even text; and (ii) how they simultaneously create text wording apart from text meaning, the two being in complementarity, with wording as the main concern. The author first illustrates the grammatical and contextual functions morphemes serve for making text process as well as those units below. Next, it carries out a case study of a sample text to observe two aspects of the present issue: (i) the selections of relevant morphemic tense options, with a few lexical items, to construct their wording textures of discourse; and (ii) the underlying accumulations of identical categories into their expanding temporality domains on the one hand and the integrations and contractions into a meaning unit of the whole text on the other, both processes being visualised as two cones in opposite directions, with the two butts joint to form a spindle, a 3-dimensional model of text as “socio-semantic unit”, a project to be further run.</p>
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 | 0.000 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".