Acquiring Vocabulary through Reading: Effects of Frequency and Contextual Richness
Bibliographic record
Abstract
While L2 vocabulary acquisition research is no longer ‘a neglected area’ (Meara, 1980), a lack of progress remains on some basic questions. One concerns the number of times a word must be encountered in order to be learned. Even using similar learning criteria, estimates range from six (Saragi, Nation, & Meister, 1978) to 20 (Herman, Anderson, Pearson, & Nagy, 1987). Another question concerns the types of contexts that are conducive to learning. Some studies have reported that rich, informative contexts are the most conducive to acquisition (Schouten-van Parreren, 1989), others that rich contexts divert attention from the lexical level and produce little acquisition (Mondria & Wit-De Boer, 1991). These phenomena were investigated in a vocabulary acquisition study with Quebec school-aged ESL learners at five levels of proficiency. First, learners read a text and were tested on its new vocabulary. Then, learned and unlearned words were compared for frequency of occurrence and level of contextual support. Frequency needs were found to be related to learner level, and contextual richness was unrelated to learning.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".