How much vocabulary is needed to use English? Replication of van Zeeland & Schmitt (2012), Nation (2006) and Cobb (2007)
Bibliographic record
Abstract
There is current research consensus that second language (L2) learners are able to adequately comprehend general English written texts if they know 98% of the words that occur in the materials. This important finding prompts an important question: How much English vocabulary do English as a second language (ESL) learners need to know to achieve this crucial level of known-word coverage? A landmark paper by Nation (2006) provides a rather daunting answer. His exploration of the 98% figure through a variety of spoken and written corpora showed that knowledge of around 8,000–9,000 word families is needed for reading and 6,000–7,000 for listening. But is this the definitive picture? A recent study by van Zeeland & Schmitt (2012) suggests that 95% coverage may be sufficient for listening comprehension, and that this can be reached with 2,000–3,000 word families, which is much more manageable. Getting these figures right for a variety of text modalities, genres and conditions of reading and listening is essential. Teachers and learners need to be able to set goals, and as Cobb's study of learning opportunities (2007) has shown, coverage percentages and their associated vocabulary knowledge requirements have important implications for the acquisition of new word knowledge through exposure to comprehensible L2 input. This article proposes approximate replications of Nation (2006), van Zeeland & Schmitt (2012), and Cobb (2007), in order to clarify these key coverage and size figures.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".