Bibliographic record
Abstract
For long, there has been debate over the appropriateness of using simplified literary texts in second language classrooms. In examining the simplified form, the main question is persuasion that is partly achieved through meta-discourse resources, which are “Self-reflective linguistic material referring to the evolving text and to the writer and the imagined reader of the text” (Hylan & Tse, 2004, p. 156). The present study compares the use of interactional meta-discourse resources (IMRs) in terms of the frequency and categorical distribution in the original copy of a novel (i.e., Oliver Twist) and its simplified counterparts. The corpus was analyzed based on the Hyland (2005) model. The frequency and categorical distribution of IMRs were calculated per 1,000 words, and the difference in their distribution was calculated using Chi-Square statistical analysis. The findings indicate a significant difference in the frequency of IMRs between the original and the simplified versions of the Dickensian novel, implying that despite having more complex syntax and semantics, the original novel seems to be more persuasive, at least on the part of IMRs, compared to its simplified counterparts. In terms of categorical distribution, there was no significant difference between them.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".