Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items
Bibliographic record
Abstract
Research into vocabulary knowledge often differentiates between breadth (how many words a person knows) and depth (how well the words are known). Both theoretical categories are essential for understanding language learners’ lexical development, but how the different aspects of vocabulary knowledge interconnect has not received the same attention as each individual dimension, especially in terms of productive knowledge. This study analyses lexis from mid-frequency lemmas in the K3–K9 frequency bands from the learner corpus PELIC (The University of Pittsburgh English Language Institute Corpus). Critically for learners, mastery of lexis in this frequency range is essential for achieving the English proficiency required for university study. From these mid-frequency items, a dataset of 7,554 tokens were collected from word families with multiple derivations and manually annotated. The findings showed high rates of collocational and derivational accuracy for the forms learners opted to use. However, compared to expert speaker texts in the Corpus of Contemporary American English (COCA), learners overused the verb forms and underused the noun forms of these lexical items. These patterns provide evidence of the interplay between breadth and depth in learners’ productive vocabulary usage, suggesting that increased lexical depth will naturally lead to greater lexical breadth and vice versa. Pedagogical implications reaffirm the importance of developing learners’ explicit morphological awareness and collocational accuracy. Suggestions for mid-frequency lexical items to prioritize in language learning are also provided, with a view to helping learners achieve academic readiness.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".