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Record W4310789512 · doi:10.16995/glossa.6386

Malagasy N-bonding: A licensing approach

2022· article· en· W4310789512 on OpenAlexafffund
Connie Ting

Bibliographic record

VenueGlossa a journal of general linguistics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsMcGill University
FundersMcGill University
KeywordsHead (geology)Austronesian languagesLinguisticsArgument (complex analysis)AdjunctionComputer scienceDislocationMathematicsPure mathematicsPhilosophyCrystallographyBiology

Abstract

fetched live from OpenAlex

This paper provides an account of N-bonding in Malagasy, a predicate-initial Austronesian language of Madagascar. N-bonding refers to a morphological process in which material from nominal arguments is morphologically bound to certain heads (Keenan 2000). I argue that N-bonding can be analyzed as a reflection of head-head adjunction configurations which can be derived in Malagasy through Local Dislocation (Embick & Noyer 2007; Levin 2015; Erlewine 2018), a post-syntactic operation that yields a complex head. Following Levin 2015, I assume that Local Dislocation is implemented in Malagasy due to licensing constraints. More specifically, I show that N-bonding occurs in all constructions in which an argument cannot be licensed by the structural mechanisms available in the language. The resulting head-head configuration then feeds a language- specific morphophonological operation that inserts a bundle of features which surface as the N-bonding element. This approach not only accounts for the distribution of N-bonding and is consistent with the observed phonological patterns, but also offers an alternative view of underlying clausal structure and voice morphology in Malagasy.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0030.007
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.035
GPT teacher head0.248
Teacher spread0.213 · 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 designTheoretical or conceptual
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

Citations2
Published2022
Admission routes2
Has abstractyes

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Same venueGlossa a journal of general linguisticsSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207