The Verb in the Linguistic and Conceptual Worldviews of English Language Speakers
Bibliographic record
Abstract
The paper studies the peculiarities of mental representations of the verb in the conceptual worldview and their reflection in the linguistic worldview of English language native speakers. Special attention is paid to the etymology and meaning of the word “verb” in the English language. In particular, its connection with the meaning “word” in general, which is manifested in the corresponding derivatives, is clarified. A tendency to define the verb through its syntactic function in scientific literature has been revealed. The key role of the predicate-argument structure, which reflects the linguistic worldview of English language native speakers, has been established. At the same time, the conceptual structure of the verb meaning and its mental representations shed light on the place of the verb in the conceptual worldview of English language native speakers. In the naive worldview of the English speakers, a tendency to associate the verb with an action or an act can be observed. High productivity of verbification in present-day English, as well as the overall high saturation of verbs in the English language corpora, estimated at 10.86 per cent, testify to the significance of the verb in English. The corpus analysis has been conducted based on the Corpus of Contemporary American English (COCA), the British National Corpus (BNC), the Strathy Corpus of Canadian English, and iWeb Corpus of modern web communication. The total distribution of verbs across the aforementioned corpora does not reveal any substantial variation. Nonetheless, the corpus data provide illuminating insights into the worldview of the native speakers of English. The prospects of further research in this direction lie in the construction of a mental model of the English verb, which would integrate the approaches to verb semantics within different modern frameworks, employing both experimental findings and empirical corpus data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".