How does lexical coverage affect the processing of L2 texts?
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
Abstract Lexical coverage, i.e. the extent to which words in a text are known, is considered an important predictor of reading comprehension, with studies suggesting 98% lexical coverage leads to adequate comprehension. However, no studies to date have examined how the various lexical coverage percentages suggested in the literature are reflected by the cognitive effort involved in processing text and the attention that is devoted to the unknown vocabulary. This study used eye-tracking to examine how lexical coverage affects the processing of text (global measures) and unknown vocabulary (word-level measures), as well as the relationship between processing time on unknown vocabulary and learning. Advanced L2 learners of English read a text in one of four lexical coverage conditions (90%, 95%, 98%, 100%) while their eye movements were recorded. Knowledge of unknown pseudowords in the texts was assessed via an immediate, meaning recall post-test. Results showed that only one of the three global measures examined showed a processing advantage for the 98% condition, reflected by longer saccades and less effortful reading than the 90% and 95% conditions. Crucially, lexical coverage did not have a significant impact on the amount of attention spent on unknown vocabulary. Processing times were found to significantly predict vocabulary gains.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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