A Study on the Effects of Lexical Processing Strategies in Incidental Vocabulary Acquisition While Reading
Bibliographic record
Abstract
Research on vocabulary acquisition in SLA has revealed that a large proportion of vocabulary is acquired without overt intention. This paper analyzes the relevance of different lexical processing strategies for incidental vocabulary acquisition while reading involving 56 native Chinese speakers who are studying English at a local university in China. The lexical processing strategies which the participants adopted to analyze unknown words are discussed based on the cognitive processes involved, namely implicit/explicit cognitive processes and top-down/bottom-up cognitive processes. According to the introspective data collected during a think-aloud protocol as well as the results of a subsequent vocabulary retention test, we examine the acquisition effects of different strategies. The results indicate that students can learn vocabulary incidentally through implicit processing, though it has a significantly lower acquisition rate than that of the explicit processing strategies. With regard to the dichotomy of top-down and bottom-up cognitive processes, the bottom-up processing strategy demonstrates better acquisition effects than the top-down strategy. Finally, a multilevel cognitive model of factors that contribute to incidental vocabulary acquisition is developed in an attempt to provide theoretical and practical implications to L2 vocabulary teaching and learning in China.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".