To what extent does productive derivational knowledge of adult L1 speakers and L2 learners at two educational levels differ?
Bibliographic record
Abstract
Abstract Research has indicated that first language (L1) English speakers acquire derivational knowledge—the ability to understand and produce derived forms of a word— through increased exposure to the language (e.g., Anglin, 1993). Second language (L2) research has shown that L2 English learners tend to have limited productive derivational knowledge in comparison to L1 speakers (Schmitt & Zimmerman, 2002). However, the degree to which productive knowledge of derivatives differs between L1 speakers and L2 learners remains unclear. Moreover, there have yet to be any studies that have compared productive derivational knowledge of L2 learners at different educational levels (undergraduate and graduate students), nor has research examined L1 and L2 production of derivatives according to the frequency levels of the target items. The present study compared the ability of 21 L1 speakers, 18 English as a second language (ESL)–speaking graduate students, and 61 English as a foreign language (EFL) undergraduate students to produce the derivatives of 30 headwords. The results indicated that L1 speakers produced significantly more derivatives than ESL graduate students and EFL undergraduate students, and ESL graduate students outperformed EFL undergraduate students.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".