L2-L1 noncognate masked translation priming as a task-specific phenomenon
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The masked translation priming effect was examined in Chinese–English bilinguals using lexical decision and semantic categorization tasks in an effort to understand why the two tasks seem to produce different patterns of results. A machine-learning approach was used to assess the participant-based factors that contribute to the sizes of translation priming effects in these tasks. As expected, the participant-based factors that predicted translation priming effects did vary across tasks. Priming effects in lexical decision were associated with higher self-rated listening, reading, and writing abilities in English. Priming effects in semantic categorization were associated with more frequent use of English in daily life, spoken English proficiency, and self-rated listening proficiency in English. These results are discussed within the framework of Multilink, the logic of which is then expanded in an attempt to account for these task differences.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.018 | 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 it