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Generative Approaches to Ergativity

2008· article· en· W2164271976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLanguage and Linguistics Compass · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsnot available
Fundersnot available
KeywordsGenerative grammarLinguisticsErgative caseObject (grammar)Subject (documents)GrammarComputer scienceHistoryPhilosophyTransitive relationMathematics

Abstract

fetched live from OpenAlex

Abstract This article surveys the principal generative syntactic analyses that have been proposed for ergativity, found primarily in Inuit, Austronesian, Mayan, and Pama‐Nyungan language families. The main puzzle for generative grammar is how to analyze the behavior of ergative and absolutive arguments in terms of the grammatical functions of subject and object. I show in this article that early approaches tend to treat the absolutive uniformly as a subject or an object, while later analyses move toward disassociating case from grammatical function. Descriptively speaking, this article identifies two types of morphological ergativity, differing in how absolutive case is assigned. Morphological ergativity is also distinguished from syntactic ergativity, which is characterized primarily by a restriction that only absolutives can undergo A’‐movement. In other aspects of the grammar, ergativity is not strikingly different from accusativity.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.106
GPT teacher head0.249
Teacher spread0.144 · 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