Suggestions toward Some Discourse-analytic Approaches to Text Difficulty: With Special Reference to ‘T-unit Configuration’ in the Textual Unfolding
Bibliographic record
Abstract
This paper represents some suggestions towards discourse-analytic approaches for ESL/EFL education, with the focus on identifying the textual forms which can contribute to the textual difficulty. Textual difficulty / comprehensibility, rather than being purely text-based or reader-dependent, is certainly a matter of interaction between text and reader. The paper will look at some of the textual factors which can be argued to make a text more or less readable for the same reader. The main focus here will be on academic texts. The high cognitive load and low readability of the expository texts in various academic disciplines will be argued to belong to certain textual strategies as well as variations in the configurations of the T-units as the prime scaffolding for the textualization process. Different categories of these variations to be discussed here will be exemplified from a few academic and expository registers. More extensive textual analyses will, of course, be necessary in order to be able to make evidential suggestions for possible correlations between certain types and clusters of T-unit configurations on the one hand, and cognitive load and readability indices on the other, across various academic registers, genres and disciplines.
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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 | 0.046 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.013 | 0.010 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".