Bibliographic record
Abstract
The aim of this study is to investigate the distribution and semantics of the most frequently used key verbs in English academic discourse based on the data from the corresponding sections of the English language corpora, i.e., the British National Corpus (BNC), the Corpus of Contemporary American English (COCA), the Strathy Corpus of Canadian English (Strathy), and the specialized British Academic Written English Corpus (BAWE). Verbs are regarded as indispensable meaningful building blocks of academic texts contributing to the implementation of their main communicative functions (Hinkel 2004; Granger and Paquot 2009b; Frels et al. 2010). The corpus-driven methodology applied in this research is based on normalized frequencies of verbs in the English corpora, providing insights into the prevailing linguistic patterns, semantic features, and common conventions that define the language used in academic contexts. As a result of the juxtaposition of reference and study corpora, key verbs are identified in the Academic sections of the British, American and Canadian English corpora, in the Written Academic section of the BNC and in its field subsections, i.e., humanities, natural sciences, political law education, social sciences, technical engineering; in the corpus of spoken academic English in comparison to written academic English corpus and in the texts of non-native English authors with different first languages. The semantic grouping of the key verbs is performed automatically using the UCREL semantic analysis system (Archer et al. 2002). Similarity groups for the discipline sections have been determined by calculating the Jaccard similarity coefficient. The comparison of verb keyness across the corpora reveals interdiscursive similarity and provides clues about the thematic and conceptual preferences across varieties of English, academic areas, and native and non-native English authors. The obtained quantitative corpus data serve as a foundation for uncovering subtle nuances of verb use in English academic discourse. This comprehension appears beneficial not only for EAP (English for Academic Purposes) learners and educators but also for promoting scholarly communication and enhancing understanding within global academic communities.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".