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Commitment to Public Leisure Service Providers: A Conceptual and Psychometric Analysis

2006· article· en· W1420727584 on OpenAlexaff
Gerard T. Kyle, Andrew J. Mowen, James D. Absher, Mark E. Havitz

Bibliographic record

VenueJournal of Leisure Research · 2006
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOperationalizationConceptualizationAgency (philosophy)Service providerStructural equation modelingSocial psychologyPsychologyPublic serviceService (business)Scale (ratio)Construct (python library)Public relationsMarketingSociologyBusinessPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

AbstractIn this investigation, we proposed and tested a scale designed to measure recreationists' commitment to public leisure Service providers. We suggested that the identity of public leisure Service providers is embedded in the facilities and settings they manage. Consequently, we feel that it is important to consider recreationists' commitment to public leisure Service providers by examining the meanings they associate with the settings and facilities managed by the agency in addition to their trust in the agency's ability to manage these settings in a manner consistent with these meanings. To operationalize this conceptualization, we drew upon the place bonding literature to construct a measure of agency commitment that consisted of five dimensions; affective attachment, place dependence, place identity, social bonding, and value congruence. Data were collected from two public leisure Service contexts: the Chattahoochee National Forest and Cleveland Metroparks. Our analyses offered strongest support for a correlated factor model consisting of the five proposed dimensions. In addition to offering a valid and reliable measure of public agency commitment, this paper also provides an example of the utility of structural equation modeling for the systematic testing of attitudinal scales.KEYWORDS: Agency commitmentscale developmentstructural equation modelingsetting attachment

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)0.006
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 score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.140
GPT teacher head0.425
Teacher spread0.285 · 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

Citations39
Published2006
Admission routes1
Has abstractyes

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