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Record W2009453525 · doi:10.1080/14927713.2008.9651401

Citizen attitudes toward advertising by public leisure service agencies

2008· article· en· W2009453525 on OpenAlexaffvenueabout
Amanda J. Johnson, Christine Tew, Mark E. Havitz

Bibliographic record

VenueLeisure/Loisir · 2008
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsGeorge Brown CollegeUniversity of Waterloo
Fundersnot available
KeywordsAdvertisingService (business)Public serviceBusinessPublic relationsSurvey data collectionMarketingPolitical science

Abstract

fetched live from OpenAlex

Abstract This study examines citizens’ attitudes toward advertising in general as well as toward public leisure service agencies. Data drawn from a five‐community survey of 497 Ontario households indicate that respondents hold mixed attitudes toward advertising in general and somewhat more positive attitudes toward advertising by public leisure service agencies. Respondents positively view public leisure service agencies’ use of advertising for information purposes, but have mixed attitudes toward the economic effects of advertising by those agencies. Using a K‐means cluster analysis, three citizen groups were identified on the basis of their involvement with public leisure services. Individuals involved at a high or functional level with public leisure services are more likely to hold positive attitudes toward advertising by these agencies as compared to those individuals who are not involved with public leisure service agencies (p < .05). The data suggest that citizens may be accepting of public leisure service agencies using a broader spectrum of promotional techniques.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.061
GPT teacher head0.305
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2008
Admission routes3
Has abstractyes

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