The role of orthography and phonology during L1 vs. L2 typed production
Bibliographic record
Abstract
Abstract Dual-route models of typing assume two pathways to retrieve a word’s spelling: a direct route connecting word to letter representations, and an indirect route via sound representations. The individual contribution of each route may depend on the modality of language acquisition: the first language (L1) is acquired sequentially in spoken and written modality respectively, whereas the second language (L2) is often acquired simultaneously in both modalities. We investigated whether sequential bilinguals rely more on the direct route during L2 vs. L1 typing. French-English bilinguals performed a typed picture-word interference task in their L1 and L2. We compared facilitation in naming for distractors that were phonologically (P) related, phonologically + orthographically (PO) related, or unrelated to the target. We predicted more facilitation by PO vs. P distractors in the L2 than in the L1. Participants showed significant facilitation by PO distractors, but not by P distractors, suggesting that orthographic overlap (together with phonology) helped retrieving the target spelling, whereas phonological overlap alone did not. The magnitude of this effect was similar across L1 and L2, contrary to our predictions. However, the absence of mere phonological facilitation suggests that phonology only contributes to typing when supported by orthography.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".