A New Computer Science Academic Word List
Bibliographic record
Abstract
This corpus-based vocabulary study aimed to develop a new computer science academic word list across ten sub-disciplines of computer science defined by Association for Computing Machinery (hereafter ACM). A corpus of Computer Science containing 2,500,990 running words was developed from 300 Computer Science Research Articles (hereafter CSRAC) as a database of this study. Drawing on and combining procedures and methods from Coxhead (2000), Gardner and Davies (2014) and other previous studies, this study developed a New Computer Science Academic Word List (hereafter NCSAWL), containing the most frequently-used computer words in computer research articles from the corpus. The NCSAWL contains 444 words, which accounts for approximately 20.33% of the coverage in the CSRAC, the NCSAWL has a much better coverage of computer English. The result of this study has numerous implications for computer science learners, English teachers, researchers, as well as material writers and course syllabus designers. For examples, English computer teachers should focus on teaching learners the most high-frequent words which have a dispersed coverage and have special meaning and use in the discipline of computer science. Teachers could also raise the awareness of learners that some words have different meanings and uses in general English. The material designers for English for academic and special purposes could incorporate the NCSAWL vocabulary into their academic reading and writing materials for computer science students. Researchers and English language teachers who are interested in expanding their computer science academic vocabulary could also use this NCSAWL.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".