Language dominance affects auditory translation priming in heritage speakers
Bibliographic record
Abstract
Late L2 learners show translation priming from the first language to the second (L1–L2), while L2–L1 effects are inconsistent (Altarriba & Basnight-Brown, 2007). Typically, late L2 learners are both less dominant in the L2 and have a later L2 age of acquisition, making the relative contribution of language dominance and age of acquisition in L2–L1 priming unclear. We test Cantonese heritage and native speakers in an auditory translation priming paradigm. As heritage speakers first learn Cantonese (L1) but later become more proficient in English (L2), this profile potentially allows for the dissociation of dominance and age of acquisition in translation priming. If age of acquisition is the primary factor, more priming is expected to occur in the L1–L2 (Cantonese-English) direction; however, if dominance plays a stronger role, priming is expected to occur in the L2–L1 (English-Cantonese) direction. Preliminary results indicate that native speakers show L1–L2 but not L2–L1 priming, consistent with previous findings, while heritage speakers show priming in both directions, but stronger L2–L1 priming. The inter-condition difference is greater for native speakers. In short, age of acquisition plays a role in bilingual language processing (Silverburg & Samuel, 2004) and potentially drives auditory translation priming effects more strongly than language dominance.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".