Learning L2 Vocabulary through Extensive Reading: A Measurement Study
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
Many language courses now offer access to simplified materials graded at various levels of proficiency so that learners can read at length in their new language. An assumed benefit is the development of large and rapidly accessed second language (L2) lexicons. Studies of such extensive reading (ER) programs indicate general language gains, but few examine vocabulary growth; none identify the words available for learning in an entire ER program or measure the extent to which participants learn them. This article describes a way of tackling this measurement challenge using electronic scanning, lexical frequency profiling, and individualized checklist testing. The method was pilot tested in an ER program where 21 ESL learners freely chose books that interested them. The innovative methodology proved to be feasible to implement and effective in assessing word knowledge gains. Growth rates were higher than those found in earlier studies. Research applications of the flexible corpus-based approach are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it