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Record W3041238614 · doi:10.3390/languages5030028

Pragmatic Uses of Negation in Chipileño Spanish (Mexico)

2020· article· en· W3041238614 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

VenueLanguages · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsNegationLinguisticsSentenceVariety (cybernetics)Scope (computer science)Repetition (rhetorical device)MathematicsComputer sciencePsychologyArtificial intelligencePhilosophyProgramming language

Abstract

fetched live from OpenAlex

This paper discusses two negation types (standard negation (SN), negative doubling (ND)) in Chipileño Spanish, a variety that has emerged as a result of contact between Spanish and Veneto (an Italo-Romance language) in Mexico. In Veneto, negation can be formed in two ways: preverbally (SN) and as a negative doubling (ND). Based on sporadic observation, bilingual speakers of Spanish and Veneto transfer a final no while speaking Spanish, a language that does not allow repetition of the same negator in the postverbal position. Using both a spontaneous and a controlled tasks, the results show two possibilities: preverbal negation only (no vino ‘[S/he] did not come’) and sentence final (no me gusta no ‘I do not like’) in both tasks. This study compares the findings from Chipileño Spanish to the other Romance varieties that exhibit similar cases of negation, while discussing its scope and relevance to discourse-pragmatic factors.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.246
Teacher spread0.222 · 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