MétaCan
Menu
Back to cohort
Record W2101422898 · doi:10.1177/0956797611436349

Personalized Persuasion

2012· article· en· W2101422898 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

VenuePsychological Science · 2012
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsKellogg's (Canada)University of Toronto
Fundersnot available
KeywordsPsychologyPersuasionPersonalityTraitSocial psychologyPersuasive communicationFraming (construction)Big Five personality traitsRegulatory focus theoryAppealCreativityComputer science

Abstract

fetched live from OpenAlex

Persuasive messages are more effective when they are custom-tailored to reflect the interests and concerns of the intended audience. Much of the message-framing literature has focused on the advantages of using either gain or loss frames, depending on the motivational orientation of the target group. In the current study, we extended this research to examine whether a persuasive appeal's effectiveness can be increased by aligning the message framing with the recipient's personality profile. For a single product, we constructed five advertisements, each designed to target one of the five major trait domains of human personality. In a sample of 324 survey respondents, advertisements were evaluated more positively the more they cohered with participants' dispositional motives. These results suggest that adapting persuasive messages to the personality traits of the target audience can be an effective way of increasing the messages' impact, and highlight the potential value of personality-based communication strategies.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.738
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.004

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 head0.224
GPT teacher head0.533
Teacher spread0.309 · 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