MétaCan
Menu
Back to cohort
Record W4200206625 · doi:10.1007/s11049-021-09532-z

Clausal embedding in Washo: Complementation vs. modification

2021· article· en· W4200206625 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

VenueNatural Language & Linguistic Theory · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of British Columbia
FundersLeibniz-GemeinschaftSogang UniversityEberhard Karls Universität TübingenWhatcom Museum
KeywordsLinguisticsSubordination (linguistics)Philosophy of languageNominalizationEmbeddingSelection (genetic algorithm)Computer scienceComplement (music)PhilosophyComplementationArtificial intelligenceMetaphysicsEpistemologyNoun

Abstract

fetched live from OpenAlex

Abstract This paper concerns clausal embedding in Washo (also spelled Washoe, Wáˑšiw), a highly endangered Hokan/isolate language spoken around Lake Tahoe in the United States. We argue that Washo offers evidence that both complementation and modification are available strategies for subordination, and in doing so contribute more generally to the ongoing debate about how clauses are embedded by attitude verbs. We observe that the embedding strategies of certain predicates in Washo follow from independent properties of clause types in the language. On the one hand, clauses embedded by presuppositional verbs come in the form of clausal nominalizations, which are selected as thematic internal arguments. The DP layer in these complements is responsible for encoding familiarity in a general sense (along the lines of Kastner 2015) both in these complement clauses as well as in other constructions in the language. On the other hand, clauses embedded by non-presuppositional verbs are not selected at all; they are instead adjunct modifiers, which follows from the fact that the attitude verbs they modify are always intransitive. This aspect of the analysis lends support to the property-analysis of ‘that’-clauses (e.g., Kratzer 2006; Moulton 2009; Elliott 2016), but only in certain instances of embedding. We argue that the Washo facts show that selection still plays a role for some verbs, contra theories that do away with it altogether (Elliott 2016), but selection cannot explain everything either, as non-presuppositional verbs are intransitive and do not select at all.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0020.000

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 head0.024
GPT teacher head0.304
Teacher spread0.280 · 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