Validating the construct of readability in EFL contexts: A proposal for criteria
Bibliographic record
Abstract
This article examines how English as a foreign language learners might be better matched to reading texts using automatic readability analysis. Specifically, I examine how the lexical decoding component of readability might be validated. In Japan, readability has been mostly determined by publishers or by professional reading organizations who only occasionally publish their lists of readability ratings for specific texts. Without transparent readability methods, candidate texts cannot be independently evaluated by practitioners. Moreover, the reliance on centralized organizations to curate from commercially available texts precludes the evaluation of the multitudes of free texts that are increasingly available on the Internet. Previous studies that have attempted to develop automatic readability formulas for Japanese learners have used surface textual features of texts, such as word and/or sentence length, and/or they have used word-frequency lists derived from large multiregister corpora. In this article, I draw upon on the findings of a study that examines how such word-lists might be validated for use in matching Japanese learners to texts (Pinchbeck, manuscript in preparation). Finally, I propose a list of general criteria that might be used to evaluate the components of readability formulas in general.
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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 | 0.088 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".