Angliškos konstrukcijos su deverbatyviniais daiktavardžiais BITE ir SNACK gimtosiose anglų kalbos atmainose: tekstyno duomenimis paremtas tyrimas
Bibliographic record
Abstract
This study investigates light verb constructions (LVCs) involving synonymous deverbal nouns, specifically focusing on the nouns bite and snack in five native varieties of English: American, British, Australian, Canadian, and New Zealand. Previous research on LVCs with synonymous nouns is limited, and their usage across different English varieties has received little attention from linguists. The aim of the research is twofold: (1) to examine the usage of LVCs with bite and snack across the five English varieties, and (2) to identify distinguishing features of these synonymous nouns in LVCs. Data were sourced from the Corpus of Global Web-Based English, and the analysis explores the combinability of bite and snack with various light verbs, as well as the modification patterns associated with each noun. The study compares frequency, types, and semantic classes of modifiers, alongside the variety and frequency of light verbs used with each noun. Both light verbs and modifiers are analysed by their distribution across the five English varieties. The findings reveal significant syntactic and semantic differences between LVCs with bite and snack. Snack combines with a broader range of light verbs than bite, and modifier patterns show that bite often implies a focus on the duration of eating, whereas snack is associated with meal size or timing. The study also highlights cross-variety differences, including the frequency and modification of LVCs, as well as preferences for light verbs and modifiers across English varieties. These insights contribute to a more nuanced understanding of LVCs and their variation in native English varieties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".