Variations in Textualization: A Cross-generic and Cross-disciplinary Study, Implications for Readability of the Academic Discourse
Bibliographic record
Abstract
According to discoursal views on language, variations in textualization strategies are always socio-contextually motivated and never happen at random. The textual forms employed in a text, along with many other discoursal and contextual factors, could certainly affect the readability of the text, making it more or less processable for the same reader. On the basis of these assumptions, the present study set out to examine how our data varied across genres and disciplines in terms of our target textual forms. These forms are as follows: the magnitude of T-unit (MOTU), the degree of embeddedness of the main verb in T-unit (DE), the physical distance between the verb and its satellite elements (PD), the magnitude of the noun phrase appearing before the verb (MOX), and the magnitude of noun phrase appearing after the verb (MOY). Our data consisted of 20 research articles randomly selected from two different disciplines of Biology and Applied Linguistics, to be analyzed in terms of the above-named textual strategies. One way ANOVA and post hoc Tukey tests were used for data analyses. The results revealed cross-generic as well as cross-disciplinary differences in the employment of the above textual forms. These findings were discussed in terms of the academic concepts and discourse on the one hand and the possible effect of the required textual forms on the readability of the text on the other hand.
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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".