How much vocabulary is needed to use English? Replication of van Zeeland & Schmitt (2012), Nation (2006) and Cobb (2007)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it