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Inference processing in discourse comprehension

2012· book-chapter· en· W2696538819 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComprehensionInferenceReading comprehensionCoherence (philosophical gambling strategy)LinguisticsNarrativeModalitiesComputer sciencePsychologyField (mathematics)Relevance theoryCognitionFocus (optics)Cognitive psychologyCognitive scienceReading (process)Artificial intelligenceSociologyPhilosophySocial science

Abstract

fetched live from OpenAlex

Abstract Discourse understanding has been systematically studied within the framework of modern cognitive psychology for fewer than forty years. The inferences that accompany discourse comprehension have been a central focus of this field. One reason for this is that virtually every aspect of language comprehension is inferential. This article describes inferential phenomena that involve augmenting explicitly stated discourse ideas with implied concepts and relations. The term “discourse” refers to coherent messages in either of these modalities. In practice, however, a majority of the research has scrutinised reading comprehension. It is also noted that researchers have inspected numerous genres of discourse, including narratives, expositions, recipes, instruction lists, and poetry. The article first discusses the construction-integration model of inference processing in discourse comprehension, coherence and its impact on inference processing, and elaborative inferences.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.995
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.281
Teacher spread0.237 · 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