A Review on Studies into Incidental Vocabulary Acquisition through Different Input
Bibliographic record
Abstract
Vocabulary acquisition, after being neglected for centuries, aroused people’s attention from the second half of last century. At that time, people began to realize, instead of grammar, vocabulary occupies the central role in language acquisition (Gass & Selinker, 1994). Compared with intentional vocabulary acquisition, incidental vocabulary acquisition was found to be the major way for people to acquire vocabularies. Early studies into incidental vocabulary acquisition focused on incidental vocabulary acquisition through reading activities. Later on, people found that listening activities was another good way to enhance incidental vocabulary acquisition. Nowadays, task mode of incidental vocabulary acquisition has become more pluralistic than before. This article is to review studies into incidental vocabulary acquisition through different input and point out the limitations of previous studies. The first limitation of previous studies is that word knowledge framework was undefined in previous studies and the second limitation is that prior knowledge, an factor which needs to be controlled, was neglected by some scholars. This review will hopefully provide some suggestions for both language teachers and language learners.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".