How well do learners know derived words in a second language?
Bibliographic record
Abstract
Abstract The study investigates derivational knowledge of second language (L2) learners as a function of four variables: learner proficiency, word family frequency, derived word frequency, and affix type as suggested by two affix difficulty hierarchies. Seventy-nine EFL learners at two proficiency levels received two tests, the VST – Vocabulary Size Test ( Nation & Beglar, 2007 ) and a custom-made ‘Derivatives Test’, which included derived forms of VST base words. We performed the following within-participant comparisons: knowledge of base words and knowledge of their derived forms, knowledge of derived forms from high-, medium, and low-frequency word families and knowledge of derivatives at different affix difficulty levels. Knowledge of basewords and their derivatives was statistically equivalent for advanced learners. However, a difference was found between the categories for less advanced learners. The findings also revealed learner proficiency and base word frequency effects, partial support for the two affix difficulty hierarchies, and no support for the effect of derivative frequency.
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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 | 0.011 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".