Cross-script orthographic and phonological preview benefits
Bibliographic record
Abstract
The present experiment examined the use of parafoveally presented first-language (L1) orthographic and phonological codes during reading of second-language (L2) sentences in proficient Russian-English bilinguals. Participants read English sentences containing a Russian preview word that was replaced by the English target word when the participant's eyes crossed an invisible boundary located before the preview word. The use of English and Russian allowed us to manipulate orthographic and phonological preview effects independently of one another. The Russian preview words overlapped with English target words in (a) orthography (ВЕЛЮР [vʲɪ'lʲʉr]-BERRY), (b) phonology (БЛАНК [blank]-BLOOD), or (c) had no orthographic or phonological overlap (КАЛАЧ [kɐ'lat͡ɕ]-BERRY; ГЖЕЛЬ [ɡʐϵlʲ]-BLOOD). The results of this study showed a clear and strong benefit of the parafoveal preview of Russian words that shared either orthography or phonology with English target words. This study is the first demonstration of cross-script orthographic and phonological parafoveal preview benefit effects. Bilinguals integrate orthographic and phonological information across eye fixations in reading, even when this information comes from different languages.
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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) | 0.004 | 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".