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 In turn, research has focused on assessing word lists to promote second language learners' comprehension of academic speech 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 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.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".