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Record W2299843808 · doi:10.1017/s0008413100003054

On the syntax of relative clauses in Korean

2013· article· en· W2299843808 on OpenAlexaff
Chung–hye Han

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSyntaxLinguisticsPronounRelative clauseSubject pronounOperator (biology)Subject (documents)Task (project management)Object (grammar)Computer sciencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract There are two main approaches to the syntax of Korean relative clauses: the operator-movement analysis and the operator-binding analysis. Although the predictions made by the two analyses are clear, no consensus is found in the literature regarding the two approaches, as there is disagreement on what the facts are. This situation thus calls for adopting a controlled experimental methodology to obtain the relevant data. In this article, I present findings from two magnitude estimation task experiments that support the operator-movement analysis. Experiment 1 tested whether a subject gap can occur in islands in relative clauses and whether it can be replaced with an overt pronoun, and Experiment 2 tested whether an object gap can occur in islands in relative clauses and whether it can be replaced with an overt pronoun. In both experiments, a gap could not occur in an island and could not be replaced with an overt pronoun. According to these findings, relativization into islands is ruled out in Korean, and thus the operator-movement analysis is supported.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Linguistics / La revue canadienne de linguistiqueSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207