A Corpus-Based Investigation of Academic Vocabulary and Phrasal Verbs in Academic Spoken English
Bibliographic record
Abstract
Academic spoken language is capturing the attention of many vocabulary researchers in recent years (Rodgers & Webb, 2016;Thompson, 2005).In turn, research has focused on assessing word lists to promote second language learners' comprehension of academic speech (Dang & Webb, 2014).The present thesis is comprised of two corpus-based studies investigating academic spoken discourse.The first study examined (1) the vocabulary demands of spoken academic English and (2) the coverage of Coxhead's (2000) Academic Word List (AWL) in spoken Academic English.Transcripts of 62 lectures and 7 seminars form the Michigan Corpus of Academic Spoken English (MICASE) were collected and analyzed.The findings suggested that coupled with proper nouns and marginal words, knowledge of the most frequent 3,000 or 7,000 word families is needed to reach 95.55% and 98.03% coverage respectively of the combined lectures and seminars corpus.The AWL provided 3.68% coverage of the combined seminars and lectures corpus.The second study compared the lexical coverage of Garnier and Schmitt's (2015) phrasal verb list (PHaVE List) with the most frequent 150 AWL lemmatized verbs in academic spoken English.The analysis was carried out on a 2,431,351 running-word corpus created from the British Academic Spoken English (BASE) and the MICASE corpora.The finding indicated that the PHaVE List accounted for slightly higher coverage figures than the AWL in the study corpus.Pedagogical implications were made for English for academic purposes teachers, learners and material designers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".