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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".