How does lexical coverage affect the processing of L2 texts?
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.
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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 | 0.000 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".