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Record W2802578375 · doi:10.1002/jcpy.1052

The Effects of Linguistic Devices on Consumer Information Processing and Persuasion: A Language Complexity × Processing Mode Framework

2018· article· en· W2802578375 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

VenueJournal of Consumer Psychology · 2018
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsUniversity of Calgary
FundersFondation HECAgence Nationale de la Recherche
KeywordsPersuasionExtant taxonMode (computer interface)Product (mathematics)PsychologyComputer scienceLinguisticsDeep linguistic processingRomanceInformation processingNatural language processingCognitive psychologyHuman–computer interactionSocial psychology

Abstract

fetched live from OpenAlex

People—be they politicians, marketers, job candidates, product reviewers, or romantic interests—often use linguistic devices to persuade others, and there is a sizeable literature that has documented the effects of numerous linguistic devices. However, understanding the implications of these effects is difficult without an organizing framework. To this end, we introduce a Language Complexity × Processing Mode Framework for classifying linguistic devices based on two continuous dimensions: language complexity, ranging from simple to complex, and processing mode, ranging from automatic to controlled. We then use the framework as a basis for reviewing and synthesizing extant research on the effects of the linguistic devices on persuasion, determining the conditions under which the effectiveness of the linguistic devices can be maximized, and reconciling inconsistencies in prior research.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.023
GPT teacher head0.358
Teacher spread0.335 · 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