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Record W2121287025 · doi:10.1080/01490400590930871

Altering Perceptions Through Repositioning: An Exercise in Framing

2005· article· en· W2121287025 on OpenAlexaffabout
Andrew T. Kaczynski, Mark E. Havitz, Ronald E. McCarville

Bibliographic record

VenueLeisure Sciences · 2005
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRecreationFraming (construction)NewspaperPerceptionPsychologyAgency (philosophy)Public relationsApplied psychologyAdvertisingSocial psychologySociologyPolitical scienceBusinessEngineering

Abstract

fetched live from OpenAlex

This research was conducted to examine the efficacy of repositioning public parks and recreation services in the public mind. Respondents were recruited at various venues throughout a large Canadian city and randomly assigned to one of five groups. After reading hypothetical newspaper format articles, respondents completed questionnaires investigating their beliefs, attitudes, and behavioral intentions regarding a local recreation agency's efforts to reduce youth crime. Four treatment messages contained various combinations of real, psychological, and competitive repositioning messages, while a control group received no information. All types of repositioning messages were effective in improving beliefs and behavioral intentions, but not attitudes. There was no significant evidence that numerical treatment messages were more effective than non-numerical messages or that the cumulative effects of various repositioning messages were more effective than a single type of message. Discussion focuses on efficacy of various framing messages, on suggestions for future research related to repositioning, and on considerations related to social marketing efforts of this nature.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.101
GPT teacher head0.455
Teacher spread0.353 · 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 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

Citations23
Published2005
Admission routes2
Has abstractyes

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