The Nativelikeness Problem in L2 Word-association Tasks: Examining Word Class and Trials
Bibliographic record
Abstract
Significant variation exists in how native speakers respond to word association tasks and challenges the usage of nativelikeness as a benchmark to gauge second language (L2) performance. However, the influence of word class and trials of elicitation is not sufficiently addressed in previous work. With controlled stimuli from multiple word classes, repeated elicitations, and analytic approaches aiming to tease apart their interactions, this study compared the extent to which native speaker controls and late L2 learners generated associates that converged to a large-scale association norm, and examined the influence of word class and trial on the likelihood to elicit idiosyncratic responses within the two language groups. During initial elicitation, only adjectives elicited greater convergence to the norm among native speakers than L2 learners. Furthermore, native speakers were more likely to generate synonyms whereas L2 learners were more likely to generate antonyms to adjectives in the initial elicitation. For nouns and verbs, 30% of associates produced by the native speaker controls failed to converge to the norm. In fact, the native speaker controls were not more “nativelike” than L2 learners for nouns and verbs until later elicitations. Finally, despite reports of significant variation among native speakers in previous work, the amount of response idiosyncrasy was consistently lower in native speakers than in L2 learners, regardless of word class or elicitation trial. By revealing the effects of word class and trials on association performance, findings from this study suggest potential means to ameliorate the issue with nativelikeness in L2 word association studies.
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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 | 0.006 | 0.009 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".