ФАКТОРЫ, ОБУСЛОВЛИВАЮЩИЕ ВЫБОР GET-ПАССИВОВ В АНГЛИЙСКОМ ЯЗЫКЕ: КОРПУСНОЕ ИССЛЕДОВАНИЕ
Bibliographic record
Abstract
This paper is part of the ongoing research project concerning the syntax of passive construction in English. Passive construction is notorious in both linguistics and education for its complex structures, varied meanings and implications, and the presence of numerous comparable constructions. To tackle these challenges, the purpose of this project is twofold. First, it contributes to a comparative study of the passive construction in English, Japanese, and other languages. Second, it contributes to pedagogy. To reach these aims, this paper uses a variationist framework to analyze a corpus of English passives. It also extends to descriptive and generative frameworks. The present study analyzes the English passives, specifically be-passives and get-passives, in Quebec, Canada, to identify the conditioning factors influencing their selection. The tokens of the passive sentences are examined in terms of their relations to age, sex, and social classes, as well as syntactic properties (e.g., agentivity). It is proposed that the choice of get-passives is affected by three independent factors: age, the presence or absence of a by-phrase, and the types of verbs used and how they are subcategorized and dynamic. Even though the corpus used in this study is fairly small, it contributes to the field by looking at a type of passive construction that has not been looked at much from different theoretical and descriptive angles. It also makes suggestions about how to do the research and what these findings mean for the form-meaning interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".