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Record W4367185864 · doi:10.3765/plsa.v8i1.5492

Juggling arguments: VSVO and other word orders in Hul’q’umi’num’ Salish SVCs

2023· article· en· W4367185864 on OpenAlexafffund
Lauren Schneider

Bibliographic record

VenueProceedings of the Linguistic Society of America · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVerbTransitive relationLinguisticsAmbiguityWord (group theory)Computer scienceWord orderElement (criminal law)Subject (documents)Feature (linguistics)Scope (computer science)Matching (statistics)Natural language processingMathematicsPhilosophyCombinatoricsProgramming language

Abstract

fetched live from OpenAlex

This paper investigates the word order of serial-verb constructions in Hul’q’umi’num’ Salish. Hul’q’umi’num’ SVCs are monoclausal constructions consisting of two or more verbs that can function as independent lexical verbs, have matching aspect, share one or more arguments, and are not connected by any linking element. Two-verb SVCs may consist of transitive and intransitive verbs. The first question concerns subject and object NP placement. For constructions with two overt NPs, an alternating VSVO pattern is both preferred in elicitation, and the only order occurring in the corpus. Only shared arguments may intervene between the verb components. Hul’q’umi’num’ SVCs exhibit flexible word order in elicitation, but certain grammatical word orders generate ambiguity. Various pragmatic strategies work together to prevent or rescue ambiguous constructions. SVCs are an understudied feature of Central Salish languages; thus investigation of this topic broadens the scope of the current literature.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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Same venueProceedings of the Linguistic Society of AmericaSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207