Individual differences in word skipping during reading in English as L2
Bibliographic record
Abstract
The Multilingual Eye-movement Corpus (MECO; Siegelman et al., 2022) contains data from unbalanced bilinguals reading in their first language (L1) for a variety of languages and in English as their second language (L2). We analyzed word skipping in L2 on the basis of five predictors consisting of the frequency and length of the word in L2 and three measures of individual differences. Besides the L2 proficiency of the participant, two novel measures were also constructed: the average amount of skipping in L1 across participants per language and whether an individual reader skips words often in their L1 compared with other L1 readers in the same language. Word skipping in L2 increased for short and high-frequency words, for participants with higher L2 proficiency, for readers whose L1 featured relatively high average skipping rates compared with the other languages, and especially for participants who skip more often in L1 than their peers. All three individual differences interacted with word length such that their influence was more pronounced for longer words. Our results show that readers prefer to maintain a certain level of word skipping resembling how they read in L1. Due to lower L2 than L1 proficiency in unbalanced bilinguals, word skipping in L2 would often be based on a comparatively less advanced stage in parafoveal word recognition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".