Readability of PISA-like Reading Texts: A Lesson Learned from Indonesian Teachers
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 article discusses the text readability of Programme for International Student Assessment (PISA)-like reading texts written by Indonesian-English-teachers who are teaching in high schools in Jawa Barat province, Indonesia. The aims of the research are to describe the lexical density, grammatical intricacy, and lexical variation indexes of the PISA-like reading texts compared to PISA reading texts 2018 released field trial new reading items. The method applied is a qualitative with descriptive quantification. The qualitative method is employed to identify and describe both lexical and grammatical words, in addition to specifying the lemmas and ranking clauses in the PISA-like reading texts. Quantifying the indexes of lexical density, grammatical intricacy, and lexical variation are implemented. All analyses utilized are based on the systemic functional linguistic approach. It is reported that, firstly, the texts of PISA reading texts 2018 are lexically denser than the PISA-like reading texts. Secondly, the texts of PISA reading texts 2018 are grammatically more intricate than the PISA-like reading texts. Lastly, PISA-like reading texts have similar index with PISA reading texts 2018.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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