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Record W4398168721 · doi:10.1075/cld.00048.new

Mandarin posture verbs

2024· article· en· W4398168721 on OpenAlexaff
John Newman, Ying Zhang

Bibliographic record

VenueChinese Language and Discourse An International and Interdisciplinary Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMandarin ChineseLinguisticsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Posture verbs have attracted considerable interest within Cognitive Linguistics. This study continues this line of research by investigating usage-based patterns associated with these verbs when used in their literal posture senses. The data for the study comes from the Lancaster Corpus of Mandarin Chinese and the CALLHOME Mandarin Corpus. We investigate the frequencies and distributions of the Mandarin posture verbs 坐 zuò ‘sit’, 站 zhàn ‘stand’, and 躺 tǎng ‘lie’ in four construction types: directional constructions, aspectual suffixes, serial verb constructions, and locative constructions. The analysis confirms the primacy of zuò, zhàn , and tǎng and, among other results, reveals a significant absence of locative phrases with the dynamic forms of the verbs as used with directionals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.008
GPT teacher head0.384
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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