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It's Fascinating Research

2008· article· en· W2030448677 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

VenueCurrent Directions in Psychological Science · 2008
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIronyPsychologyUtteranceComprehensionMeaning (existential)Construct (python library)LinguisticsTone (literature)Autism spectrum disorderSalientInterpretation (philosophy)Cognitive psychologyAutismDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Verbal irony is nonliteral language that makes salient a discrepancy between expectations and reality. For researchers who study verbal irony, a critical question is: How do we grasp the meaning of ironic language? The parallel-constraint-satisfaction approach holds promise as an answer to this question. By this account, multiple cues to ironic intent, such as tone of voice, incongruity, and knowledge of the speaker, are processed rapidly and in parallel and this information is coordinated with the utterance itself in order to construct a coherent interpretation that is the best fit for the activated information. Recently, research with individuals who struggle with irony comprehension (typically developing children, individuals with autism-spectrum disorder, individuals with brain injury) has provided new clues about the complex process by which ironic meaning is inferred.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.227
GPT teacher head0.514
Teacher spread0.288 · 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