Acquiring Vocabulary through Reading: Effects of Frequency and Contextual Richness
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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