Bibliographic record
Abstract
Abstract Acquiring vocabulary knowledge is a vital part of L2 learning because vocabulary plays a significant role in every mode of communication (reading, listening, writing, and speaking). For learners to become independent users of a L2, they must know many thousands of words and learn how to use them well in communication. For example, learners of English must acquire up to 9,000 words (e.g., happy) and their morphologically-related forms (e.g., happiness, unhappy, happily) to comprehend spoken and written texts (e.g., conversation, television programs, films, novels, and newspapers) ( Nation, 2006 ; Webb & Rodgers, 2009a , 2009b ). Moreover, acquisition of L2 vocabulary entails learning different aspects of word knowledge such as word parts, collocations, and associations, not only learning form-meaning connections. Thus, the teaching, learning, and researching of L2 vocabulary can be highly complex. The purpose of this chapter is to provide a guide to researching instructed second language vocabulary acquisition. The chapter sets out to provide (1) an overview of key concepts in vocabulary research, (2) a brief overview of L2 vocabulary research focusing on intervention studies, (3) an overview of a frequently employed study design (pretest-posttest design), different measures for assessing L2 vocabulary knowledge as well as options and cautions for interpreting data, (4) advice for future vocabulary researchers, and (5) tips to overcome potential challenges.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.213 | 0.122 |
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".