How well do learners know derived words in a second language?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 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 it