Intralingual and interlingual effects in a pure language list
Bibliographic record
Abstract
Abstract In various English lexical decision tasks (LDTs), bi-/multilinguals have evinced shorter response times (RTs) for cognates (i.e., words with the same meaning in two languages, e.g., the Dutch-English water ) and longer RTs for interlingual homographs (IHs; words with distinct meanings in two languages, e.g., the Dutch-English map ) compared to monolingual controls (e.g., Biloushchenko, 2017 ). This suggests that multilinguals automatically activate lexical representations from multiple languages ( Dijkstra et al., 1998 ). To further investigate language (non-)selectivity, in our English LDTs, we compare the processing of cognates and IHs to intralingual words that are similar but only exist in English (i.e., cognates to metonyms like chicken , which can refer to the animal and the closely-related sense “chicken meat”, and IHs to homonyms like bat , which has two meanings: “baseball bat” and “nocturnal flying animal”). Half of our cognates and IHs only exist in our native Dutch participants’ non-native languages (English-French) to avoid any potentially confounding effects of the supposed “special status” ( Midgley et al., 2011 ) of L1. Significant inhibition was found for homonyms and significant facilitation for metonyms and native (Dutch-English) cognates but not for non-native (English-French) cognates. These results are discussed in relation to the language non-selective hypothesis ( Dijkstra et al., 1998 ).
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".