Bilingual lexical access in context: Evidence from eye movements during reading.
Bibliographic record
Abstract
Current models of bilingualism (e.g., BIA+) posit that lexical access during reading is not language selective. However, much of this research is based on the comprehension of words in isolation. The authors investigated whether nonselective access occurs for words embedded in biased sentence contexts (e.g., A. I. Schwartz & J. F. Kroll, 2006). Eye movements were recorded as French-English bilinguals read English sentences containing cognates (e.g., piano), interlingual homographs (e.g., coin, meaning corner in French), or matched control words. Sentences provided a low or high semantic constraint for target-language meanings. Both early-stage comprehension measures (e.g., first fixation duration, gaze duration, and skipping) and late-stage comprehension measures (e.g., go-past time and total reading time) showed significant cognate facilitation and interlingual homograph interference for low-constraint sentences. For high-constraint sentences, however, only early-stage comprehension measures were consistent with nonselective access. There was no evidence of cognate facilitation or interlingual homograph interference for late-stage comprehension measures. Thus, nonselective bilingual lexical access at early stages of comprehension is rapidly resolved in semantically biased contexts at later stages of comprehension.
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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 | 0.002 |
| 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) | 0.001 | 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".