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Record W3088083909 · doi:10.33137/twpl.v39i0.28778

The meaning of antipassive: Evidence from Australian languages

2017· article· en· W3088083909 on OpenAlexaffvenue
Jessica Denniss

Bibliographic record

VenueToronto Working Papers in Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReading (process)MorphemeMeaning (existential)Scope (computer science)LinguisticsInterpretation (philosophy)Event (particle physics)Object (grammar)Computer scienceHistoryPhilosophyEpistemologyProgramming language

Abstract

fetched live from OpenAlex

Existing semantic analyses of the antipassive morpheme capture scope facts but are unable to handle the non-culminating event reading which is found in Australian languages (among others). I develop a meaning for the antipassive that is able to capture this reading, and also account for the unexpected high-volitionality reading also found in some Australian languages. Finally, I turn to a typologically rare situation in two languages in which the antipassive construction is interpreted as denoting a culminated event with a fully affected object. I provide an account for this and show that there are formal similarities between the two kinds of antipassive, which may explain why the antipassive operator is used in the latter case even though it produces an interpretation of events which is apparently opposite to the more common non-culminating reading.

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)0.010
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0020.004
Open science0.0010.004
Research integrity0.0010.002
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 head0.052
GPT teacher head0.305
Teacher spread0.253 · 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

Citations90
Published2017
Admission routes2
Has abstractyes

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