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Noun Incorporation

2017· other· en· W4252325963 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Wiley Blackwell Companion to Syntax, Second Edition · 2017
Typeother
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSyntaxNoun phraseLinguisticsNounVerbComputer scienceLexiconNominalizationSet (abstract data type)Natural language processingArtificial intelligencePhilosophyProgramming language

Abstract

fetched live from OpenAlex

Abstract: This chapter provides an overview of noun incorporation in broad terms, examining nominals found either within or strictly adjacent to predicates from numerous languages. Syntactic issues have shifted from an earlier debate about whether noun incorporation was an operation in the lexicon or syntax to more recent discussion about whether noun incorporation is a narrow syntax or PF operation. Other issues concern whether phrases or just heads can incorporate. If phrases incorporate, are they phrasal at the point of incorporation? AGREE analyses examine the trigger for incorporation. A number of analyses posit that there is no movement of the nominal at all (pseudo noun incorporation), and utilize an adjacency relation between the verb and incorporated nominal. It remains clear that languages show similar cross‐linguistic properties in noun incorporation, even to the extent that language‐internal differences are often similar, often relating to verb class. The expansion of the set of empirical data from semantic analyses and other work continues to lead toward refinements.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0360.008

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 head0.028
GPT teacher head0.241
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