The Comprehensibility of Readable English Texts and Their Back-Translations
Bibliographic record
Abstract
This paper presents the results of a study initiated by the potential employment of readability measures to assess the equivalence of reading ease and grade level indices between source texts and their translations as well as back-renderings. It was questionable whether there was a causal relation between the indices and their comprehensibility levels, because whereas the former concentrated merely on quantities of linguistic elements and their formal relations, the latter considered such factors as particular characteristics of each element, meaning coverage, and readers’ socio-psychological background. This study aimed to disclose the relation between the readability measures and the comprehensibility levels of source texts and their translations, as well as back-renderings. A number of English texts, along with their translations in Indonesian, were deliberately chosen for that purpose. The translations were then back-rendered to the source language utilizing Google Translate. Comparison between the source texts and their translations as well as back-renderings was capable of showing their similarities in the readability levels and average number of characters, words, sentences, and words per sentence in the texts. And asking prospective readers about their perception concerning their understanding of such texts was capable of disclosing the causal relation between the readability and the comprehensibility levels of the texts.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".