Measuring L1 and L2 Productive Derivational Knowledge: How Many Derivatives Can L1 and L2 Learners with Differing Vocabulary Levels Produce?
Bibliographic record
Abstract
Abstract Derivational knowledge, the ability to understand and produce derivatives of a word, is essential for vocabulary learners to expand their lexical knowledge. Earlier research (e.g., Schmitt & Zimmerman, 2002) has shown that L2 learners may have limited ability to produce derivatives compared to L1 speakers. However, the degree to which productive derivational knowledge differs between L1 and L2 learners, and among learners at different levels of vocabulary knowledge has yet to be examined. The present study investigated the extent to which L1 English speakers (n = 23) and L2 English learners (n = 107) at varying vocabulary levels (1000‐5000) could produce the derivatives of 90 headwords in a decontextualized derivative recall test. A generalized linear mixed model indicated that L1 and L2 productive derivational knowledge significantly differed, and L2 productive derivational knowledge differed among learners with different vocabulary levels. However, the results revealed that the L1 speakers and the learners who had mastered the higher vocabulary levels (3000–5000) produced a similar number of derivatives in the decontextualized recall test. The findings suggest that learners’ vocabulary levels could be indicative of L2 productive derivational knowledge to some degree. Lastly, the results are discussed to provide pedagogical implications for teaching and assessing L2 productive derivational knowledge.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".