Second-language processing of English mass-count nouns by native-speakers of Korean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study deals with the English mass-count distinction and how it cues meaning. 40 native speakers of Korean processed common nouns in their L2 (English) and in Korean. English native speakers performed the same English task. Anglophones individuated both count nouns and mass nouns denoting collections of entities. They were also acutely sensitive to plural-marking as a cue to the meaning of ambiguous “flexible” nouns denoting either bounded entities or substances. Koreans were target-like on 3 classes of English nouns but were insensitive to plural-marking on English flexible nouns. A comparison of English- and Korean-language tasks revealed that Koreans were using the same types of responses on semantically similar Korean and English items, consistent with the hypothesis that they use lexical semantics (not grammar) to arrive at an interpretation. Our study shows that Koreans perform at native-like levels on a judgement task involving the 3 most common classes of English nouns while remaining insensitive to English plural-marking. Learners do not make use of the mass-count syntax of English to interpret common nouns and appear not to have learnt plural-marking.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it