Readability of PISA-like Reading Texts: A Lesson Learned from Indonesian Teachers
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.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".