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Record W4315648716 · doi:10.16995/glossa.8574

Concessive scalar particles: Symmetric vs. non-symmetric alternatives. The case of Spanish <em>siquiera</em>

2023· article· en· W4315648716 on OpenAlexaff
Luis Alonso‐Ovalle, Elizabeth Heredia Murillo

Bibliographic record

VenueGlossa a journal of general linguistics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsMcGill University
FundersUniversity of Massachusetts Amherst
KeywordsPolarity (international relations)Variation (astronomy)Scalar (mathematics)LinguisticsVariety (cybernetics)ModalMathematicsPure mathematicsPsychologyPhysicsPhilosophyStatisticsAstrophysicsGeometryChemistry

Abstract

fetched live from OpenAlex

Polarity items have been analyzed as existential quantifiers that introduce alternatives into the semantic derivation (Krifka 1991; 1995; Kratzer & Shimoyama 2002; Chierchia 2013). Under this approach, variation in the polarity system can be reduced to variation in the types of alternatives that polarity items introduce (Chierchia 2013). We present a case of dialectal variation within the polarity system that can be treated along these lines. The paper focuses on Spanish siquiera, a concessive scalar particle. Concessive scalar particles (Slovenian magari, Greek esto, and Spanish siquiera) (Crnič 2011a; b) are polarity items licensed in a variety of downward entailing environments, where they can be paraphrased with English even, and in modal environments, where they can be paraphrased with English at least. The paper shows that the interpretation and distribution of Spanish siquiera differs between Iberian and Andean Spanish. We propose that siquiera shares the same assertive core across dialects, but differs in the types of alternatives that it introduces.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.006
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.028
GPT teacher head0.317
Teacher spread0.289 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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