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Record W2806297929 · doi:10.3968/10375

A Contrastive Study of English and Chinese Synthetic Compounds

2018· article· en· W2806297929 on OpenAlexvenueno aff
Wang Wei, Weihong Zhou

Bibliographic record

VenueCanadian social science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsSyntaxMorphology (biology)LinguisticsWord orderComputer scienceOrder (exchange)Natural language processingPhilosophyBiology

Abstract

fetched live from OpenAlex

The current research compares the distributions of OV/VO patterns in English and Chinese synthetic compounds in a quantitative way and attempts to explain the mechanisms of the two patterns of compounds in English and Chinese. Both English and Chinese adopt SVO as their basic word order. However, the interface of syntax and morphology is more clear-cut in English than in Chinese. VO order prevails in English syntax. But VO order must be transformed into OV order in morphology. VO order is quite unproductive in English morphology. Compared with English, the boundary between morphology and syntax is rather vague in Chinese. The formation of Chinese compounds could be explained by the theory of “syntax-as-morphology”, i.e., the VO order of syntax is directly copied by morphology in Chinese. The mechanism of Chinese synthetic compounds provides evidence for the hypothesis of “syntax as morphology”.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.304
Teacher spread0.290 · 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 teacher head, not a consensus.

Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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