The Pragmatic Motivation Behind the Generating Mechanism of New “Bei +X” Construction
Bibliographic record
Abstract
Recently, the new “bei +X” as a catch phrase has been gained much attention of researchers, such as “被代表” (being represented), “被幸福” (being happy), 被高铁 (being high speed rail) etc.. The new “bei+X” construction adopts the form of traditional “bei+X” construction, but applies the words and expressions which are not allowed in traditional “bei+X” construction in order to achieve pragmatic negation with distorted language form and a stressed “bei”. Based on this new construction, people can satirize some abnormal social phenomena and express their impotence. Previous studies of this construction mainly centered on forms and usages, and with more and more such kind of phrases appear, researchers began to study the generating mechanism of this construction. However, most previous studies of it are based on construction grammar and the study from the perspective of pragmatics mainly focuses on pragmatic effects and values while the generating mechanism is less explained. Therefore, this paper aims to explore the pragmatic motivation behind the generating mechanism of new “bei +X” in some specific principles: contexts constrain, requirement for the maxim of minimization, and face-saving. The findings will provide a complementary explanation of the generating mechanism of new “bei +X” construction and enable us to have a better understanding of this new construction.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".