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Record W2656092875

The Instrumental: dative and its double

2016· article· en· W2656092875 on OpenAlexaff
Ludovico Franco, M. Rita Manzini

Bibliographic record

VenueFlorence Research (University of Florence) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsCanadian Linguistic Association
Fundersnot available
KeywordsDative caseComputer scienceLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

We will argue that instrumentals are the mirror image of dative/genitive obliques. We propose
\nthat both sets of adpositions/cases are elementary predicates, expressing a zonal inclusion (partwhole/possession
\nrelation); instrumentals reverse the direction of the relation with respect to
\ndatives/genitives. Our claim is that with-type morphemes provide very elementary means of
\nattaching extra participants (themes, initiators, etc.) to events (VP or vP predicates) – with specialized
\ninterpretations derived by pragmatic enrichment (contextual, encyclopedic) at the C-I
\ninterface. We will extend our proposal to account for the observation that the instrumentals can
\nbe employed cross-linguistically in triadic verb constructions alternating with datives and we
\nwill broaden our discussion to account for dative/instrumental syncretism (eventually including
\nDOM objects), arguing that the inclusion predicate (⊆) corresponding to ‘to’ or dative case and its
\nreverse (⊇), corresponding to ‘with’ or instrumental case, may reduce to an even more primitive
\ncontent capable of conveying inclusion in either direction. Finally, we will address ergative alignments,
\nshowing that languages may attach external arguments/agents either as possessors (⊆)
\nor as causers (⊇) of a given event/state, yielding the two most widespread patterns of syncretism
\nof the ergative morpheme, that is with either instrumentals or genitives/datives.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.130
GPT teacher head0.292
Teacher spread0.163 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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