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Record W3107721665 · doi:10.1075/ftl.10.06col

On why people don’t say what they mean

2020· book-chapter· en· W3107721665 on OpenAlexaff
Herbert L. Colston

Bibliographic record

VenueFigurative thought and language · 2020
Typebook-chapter
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyMathematics

Abstract

fetched live from OpenAlex

Abstract The study addresses two problems with recent psycholinguistic research on why people don’t say what they mean, (1) possible underrepresentation in research studies of types of figurative language found in everyday talk, and (2) potential ecological validity problems due to using standard psycholinguistic experimental methodologies and inauthentic language materials. In three experiments, these problems were addressed using authentic productions of a relatively unexplored figurative language type – formulaic language, specifically gratitude acknowledgements , which cover a range of figurativity (e.g., “don’t worry about it”, through, “anytime”), often using hyperbole as part of their functioning – a key focus of the present study. The results demonstrate that speakers use figurative gratitude acknowledgements to achieve the pragmatic effects of politeness and esteem display as well as fondness expression, which are not achieved to the same extents by nonfigurative gratitude acknowledgements. The particular pragmatics of this figurative form, the influence of these pragmatic effects on some theoretical questions, and the broader implications of inclusion of new figurative language forms, as well as authentic language items and methods, in research on figurative language production and pragmatics, are discussed.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.230
Teacher spread0.216 · 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 teacher head, not a consensus.

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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