Suggestions toward Some Discourse-analytic Approaches to Text Difficulty: With Special Reference to ‘T-unit Configuration’ in the Textual Unfolding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it