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Record W4411347253 · doi:10.1177/14614456251341408

Voiced illustrations: The use of constructed voices in the study of argument

2025· article· en· W4411347253 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

VenueDiscourse Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsArgument (complex analysis)LinguisticsConversation analysisIndirect speechDiscourse analysisSociologyPsychologyPhilosophyConversation

Abstract

fetched live from OpenAlex

A defining characteristic of discourse studies as a field is its grounding in attested data and rejection of introspective data. Researcher intuition or speculation about what discourse is like does not constitute evidence. However, the use of invented examples is not uncommon in the study of argument, a phenomenon that has received little attention. Rather than dismiss them on epistemological grounds, this paper views invented examples as a feature of the written discourse of researchers and investigates the purpose they serve as ‘voiced illustrations’. Based on an analysis of 578 voiced illustrations in 26 published argumentation research articles, the study shows that constructed voices – fictional or hypothetical voices invented by a writer or speaker – are common and explains how they are used to illustrate abstractions. This use of constructed voices in research papers bears intertextual traces of the textbook genre, a form of generic intertextuality.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.192

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 integrity0.0000.000
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 head0.079
GPT teacher head0.392
Teacher spread0.313 · 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