Extensive Reading: A Stimulant to Improve Vocabulary Knowledge
Why this work is in the frame
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Bibliographic record
Abstract
Extensive reading, ER, can be considered as a good learning technique to improve learners' vocabulary knowledge. Bell (2001) states that ER is a type of reading instruction program used in ESL or EFL settings, as an effective means of vocabulary development. The subjects participated in this study were 40 upper-intermediate and 40 lower-intermediate learners drawn from a population through a proficiency test to see if ER helps them improve their vocabulary knowledge at the above-stated levels. To this end, at each level an experimental and a control group (EG and CG) were formed each of which comprised 20 subjects randomly selected and assigned. All the conditions especially teaching materials were kept equal and fixed at each level, except for the EG the subjects were given five extra short stories to read outside for ten weeks. The results showed that EG at both levels indicated improvement in their vocabulary learning after the experiment. Key words: Extensive Reading; Reading; Vocabulary improvement
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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