Review of Schmitt, N., & Schmitt, D. (2020). Vocabulary in language teaching (2nd ed.). Cambridge University Press.
Bibliographic record
Abstract
Vocabulary is one of the most popular topics in second language (L2) research, with most of the scholarly activities occurring in the past 20 years.Despite the plethora of published studies, few scholars have made the research as accessible to practitioners as Norbert and Dianne Schmitt have.Drawing on years of hands-on experiences and research in the field, the second edition of Vocabulary in Language Teaching is again a valuable contribution.The authors provide a comprehensive overview of the topic which enables teachers to make informed choices about what is important in vocabulary teaching and learning.Among the book's prominent themes, connections are drawn between empirical research, theory, the nature of lexical development and the learner's lexical network, classroom practice, and the central role of vocabulary in L2 instruction.These major themes pervade the nine sections, and pedagogical applications are suggested at the end of each chapter.This volume comprises nine chapters, each contributing to a deeper understanding of vocabulary acquisition, instruction, and assessment.Chapter 1 serves as a general explanation of what is meant by the term vocabulary.It addresses the challenge of attempting to fully conceptualize the term and the different ways that "word" can be operationalized as a counting unit in the literature.The authors also discuss research findings as they pertain to determining the number of lexical items that an English language learner would need to be a proficient user of the language.Chapter 2 presents a historical overview of L2 vocabulary instruction.The discussion ranges from the Romans to the current era, which serves to situate modern day vocabulary instruction by describing what has led it to its current state.The authors, for example, describe methods that have fallen out of favor to emphasize the need for more vocabulary-based instruction in L2 teaching.In Chapter 3, the authors describe vocabulary knowledge as a series of levels of learning that fall on a continuum.They offer an in-depth discussion of the multidimensionality and complexity of word knowledge.They discuss the components of lexical knowledge as including the spoken form, written form, part of speech, derivative forms, grammatical patterns, collocations, frequency, register, and as a semantic network of associations.Vocabulary acquisition is thus described as long-term process, and extensive recycling is required to master the different stages and facets of word knowledge.This second edition provides an in-depth argument for the advantages of corpora and corpus-based evidence.Chapter 4 describes how advances in corpus-linguistic methodology have led to the development of novel word-frequency lists, and new information on collocations and formulaic language.The authors argue that intuition-based supposition in vocabulary research in the past are now confirmable through analysis of large and representative text corpora by using widely available and user-friendly corpusanalytical software.Chapter 5 continues this discussion of corpora with a specific focus on wordlists.The authors discuss how vocabulary occurs in different domains of use and performs different functions in texts.High-frequency words, academic words, technical words, and low-frequency words are presented as major categories that play different roles in learners' communicative needs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".