Bibliographic record
Abstract
Abstract The linguistic features of academic spoken English are different from those of academic written English. Therefore, for this study, an Academic Spoken Word List (ASWL) was developed and validated to help second language (L2) learners enhance their comprehension of academic speech in English‐medium universities. The ASWL contains 1,741 word families with high frequency and wide range in an academic spoken corpus totaling 13 million words. The list, which features vocabulary from 24 subjects across four equally sized disciplinary subcorpora, is graded into four levels according to Nation's British National Corpus and Corpus of Contemporary American English lists, and each level is divided into sublists of function words and lexical words. Depending on their vocabulary levels, language learners may reach 92–96% coverage of academic speech with the aid of the ASWL. Open Practices This article has been awarded Open Materials and Open Data badges. The composition of the corpora, the Academic Spoken Word List, and sublists are publicly accessible via the Open Science Framework at https://osf.io/gwk45 and the IRIS digital repository at http://www.iris‐database.org . Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki .
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.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.024 |
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".