Use of Syntactic Elaboration Techniques to Enhance Comprehensibility of EST Texts
Bibliographic record
Abstract
The current study examined differential effects of two pre-modification types, syntactic elaboration and syntactic simplification (at the level of syntax and irrespective of problematic lexis), on EST students’ reading comprehension. The purpose was to see whether a priori syntactic elaborative adjustment, given its advantages over simplification, can augment comprehensibility of scientific texts in order to replace simplification adjustment. To carry out the study, three versions of 5 passages including Baseline, syntactically simplified, and syntactically elaborated were provided. All the five passages were relevant to civil engineering and they were modified using two above-mentioned techniques. The subjects of the study were composed of 185 homogenous civil engineering students who participated in different phases. The results revealed that syntactic simplification and syntactic elaboration procedures operated nearly in the same way in orienting the EST texts toward comprehensibility. The results of the study even indicated that students benefited more from elaborated than simplified texts although it was not statistically significant. Therefore, the study supports the view that syntactic elaborative adjustment can be exercised in advance on EST materials for pedagogical purposes since it increases the reading comprehension at the same time keeps unfamiliar syntactic units intact to be learned by EST readers
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".