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Record W2490303674 · doi:10.1075/la.208.08hed

Multiple focus and cleft sentences

2013· book-chapter· en· W2490303674 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

VenueLinguistik aktuell · 2013
Typebook-chapter
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFocus (optics)PresuppositionLinguisticsSentencePropositionComputer sciencePsychologyPhilosophyPhysics

Abstract

fetched live from OpenAlex

The information structure of English cleft sentences is discussed. A cleft sentence divides a proposition into two parts, which are interpreted as an exhaustive focus and a pragmatic presupposition. These two semantic components can be flexibly mapped onto the information structure categories of topic and comment to arrive at comment-topic (‘stressed focus’) clefts and topic-comment (‘informative presupposition’) clefts. Clefts thus introduce a cleft focus or even a pair of foci constructionally. They also exhibit an assertive (comment) focus, which may or may not correspond to the cleft focus. While only exclusive focus particles can associate with the cleft focus, additive and scalar focus particles can associate with the assertive focus in the cleft clause, thus giving rise to additional cleft sentences containing multiple instances of focus. Keywords: cleft; focus; presupposition; topic

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0060.002

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 head0.218
Teacher spread0.190 · 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