English Morphemic Constituents Working for Discourse Wording: Extending Rank Scale from “Clause (Complex)” up to “Text (Type)”
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".