Bibliographic record
Abstract
Abstract Although there is a growing interest in assessing how much L2 receptive affix knowledge learners have, research testing this knowledge using an extensive, standardized measure is relatively scarce. This study tested 21 L1 and 107 L2 learners of English to assess receptive knowledge of forms, meanings, and grammatical functions of 118 derivational affixes. Participants’ responses on the Word Part Levels Test were analyzed in mixed effects logistic regression models that examined the effects of affix difficulty, affix knowledge aspect, and vocabulary levels in predicting response accuracy. Results indicated that L1 and L2 affix knowledge differed depending on affix difficulty and knowledge aspect, L2 affix knowledge increased as a function of L2 vocabulary levels, and there was a clear difference between learners that had not mastered the first 1,000 frequency level and the rest of the learners. This suggests that developing the knowledge of the highest frequency vocabulary is critical to improve affix knowledge. The importance of using standardized measures of vocabulary in teaching and researching vocabulary knowledge is also discussed.
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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 | 0.009 |
| 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 | 0.000 | 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".