Morphological knowledge in English learner university students is sensitive to language statistics: A longitudinal study
Bibliographic record
Abstract
Abstract Exposure to statistical patterns of language use affects language production and comprehension. In this longitudinal study of English language learner (ELL) university students, we examined the interplay between language experience and language statistics as a window into the formation and stability of morphological representations in memory. We hypothesized that within-participant change in sensitivity to distributional properties of complex words on written production would reflect changes in morphological knowledge. At two timepoints, separated by 8 months of language exposure, a sample of ELLs (n = 196) completed a written suffix completion task. The largest gains in production accuracy were observed for derived words ending in less productive suffixes. In addition, across both timepoints we found a consistent effect of derivational family entropy, such that derived words belonging to morphological families with equally dominant members were less accurately produced. Both effects indicate that ELLs exploit distributional cues to morphological structure and shed light on two aspects of morphological knowledge in ELLs. First, knowledge of suffixes becomes more entrenched in memory, independently of knowledge of the full forms of derived words. Second, ELLs draw upon interlexical connections between morphological family members during written word production.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".