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Pragmatics of Focus

2016· reference-entry· en· W2525247930 on OpenAlexaboutno aff
Jon Stevens

Bibliographic record

VenueOxford Research Encyclopedia of Linguistics · 2016
Typereference-entry
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsFocus (optics)UtteranceSentenceContext (archaeology)PragmaticsStress (linguistics)Pitch accentInterrogativeAffixSet (abstract data type)Computer scienceHistoryProsodyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Generally speaking, ‘focus’ refers to the portion of an utterance which is especially informative or important within the context, and which is marked as such via some linguistic means. It can be difficult to provide a single precise definition, as the term is used somewhat differently for different languages and in different research traditions. Most often, it refers to the linguistic marking of (i) contrast, (ii) question-answering status, (iii) exhaustivity, or (iv) discourse unexpectability. An illustration of each of these possibilities is given below. In English, the focus-marked elements (indicated below with brackets) are realized with additional prosodic prominence in the form of a strong pitch accent (indicated by capital letters). (i) An [AMERICAN] farmer met a [CANADIAN] farmer…(ii) Q: Who called last night?A: [BILL] called last night.(iii) Only [an ELEPHANT] could have made those tracks.(iv) I can’t believe it: The Ohioans are fighting [OHIOANS] ! The underlying intuition common to all these instantiations is that a focus represents the minimal information needed to convey an important semantic distinction. Focus can be signaled prosodically (e.g., in the form of a strong pitch accent), syntactically (e.g., by moving focused phrases to a special position in the sentence), or morphologically (e.g., by appending a special affix to focused elements), with different crosslinguistic focus marking strategies often carrying slightly different restrictions on their use. Example (i) evokes a set of two contrasting alternatives, {‘American farmer,’ ‘Canadian farmer’}, and the meaning ‘farmer’ is common to both members of the set. That is, within this evoked set of alternatives, ‘farmer’ is redundant, and it is the nationality of the farmers which differentiates the two people. Example (ii) exhibits a similar property. One of the standard theories of question semantics represents questions as sets of possible appropriate answers. For (ii), this would be a set of propositions like {‘Bill called last night, ‘Sue called last night,’ etc.}. As with (i), there is an evoked set of meanings whose members share some overlapping semantic material. Within this set, the verb phrase meaning ‘called last night’ is redundant, and it is the identity of the subject that serves to differentiate the true answer. Example (iii) demonstrates a relationship between focus and certain words like only. The sentence means something like ‘of all the animals who might have made these tracks, it must be an elephant.’ As with (i) and (ii), this involves a set of alternatives: the set of possible track makers. That the sentence serves to single out a unique member of this set as being the true track maker makes the subject an elephant a natural focus of the sentence. Finally, in (iv), we see that focus on ‘Ohioans’ is being used to contrast the semantic content of the sentence with some preconception, namely that Ohioans are unlikely fighters of Ohioans. Examples (iii) and (iv) point to more specific uses of focus in different languages. In Hungarian, so-called identificational focus, which is marked syntactically, requires an exhaustive interpretation, as if a silent only were present. And in some Chadic languages, a meaning of “discourse unexpectability,” as in (iv), is required to mark focus via syntactic or morphological means.

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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.018
Scholarly communication0.0070.012
Open science0.0010.007
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.070
GPT teacher head0.341
Teacher spread0.271 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations6
Published2016
Admission routes1
Has abstractyes

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Same venueOxford Research Encyclopedia of LinguisticsSame topicLexicography and Language StudiesFrench-language works237,207