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Record W2626106285

Gaining attention: The effects of message framing on attention towards physical activity messages

2011· article· en· W2626106285 on OpenAlexaffabout
Erin M Berenbaum, Amy E. Latimer‐Cheung, Monica S. Castelhano, Effie J. Pereira

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsQueen's University
Fundersnot available
KeywordsFraming (construction)PsychologyEye trackingPhysical activityFraming effectSocial psychologyVisual attentionAdvertisingCognitive psychologyComputer scienceCognitionMedicinePersuasionEngineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Previous research has demonstrated that physical activity messages are more effective when presented in a gain-framed manner (Latimer et al. 2010). However, the factors through which this facilitating effect occurs is unclear. The amount of attention directed towards the physical activity messages may play an important role in the framing effect (Petty et al., 2002; Bassett et al., 2011). The present study examined how the framing of a message impacts viewers' attention towards advertisements promoting physical activity. Thirty undergraduate students aged 18-35 viewed 10 gain- and loss-framed advertisements while their eye movements were recorded using eye-tracking technology. Attention was measured by examining eye fixations and dwell time. RM ANCOVAs revealed that the framing of the message significantly affected both fixation and dwell time measures. Participants had significantly more fixations per word (p Acknowledgments: Funding: Social Sciences and Humanities Research Council, Canada Research Chairs Program

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.047
GPT teacher head0.355
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2011
Admission routes2
Has abstractyes

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