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Record W4302027595 · doi:10.1177/15588661221077695

If You Build it Will They Come? The Mediating Roles of Human Resource and Program Capacities

2022· article· en· W4302027595 on OpenAlexaff
Kevin Wilson, Laura Wood, Ryan Snelgrove

Bibliographic record

VenueRecreational Sports Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRecreationMediationBusinessResource (disambiguation)Human resourcesCapacity buildingEnvironmental economicsBootstrapping (finance)MarketingPublic relationsComputer sciencePolitical scienceEconomic growthManagementEconomicsFinance

Abstract

fetched live from OpenAlex

The amenity arms race among post-secondary institutions is driving the new development or expansion of campus recreation facilities. However, investing in new and larger campus recreation facilities may not necessarily translate into usage and ultimately provide the associated benefits to students. This study explored whether human resource capacity and program capacity are mechanisms that help explain the conditions under which facility capacity translates into facility usage. Secondary data were obtained from NIRSA's research and assessment initiative from post-secondary institutions in the United States ( n = 103) that contained measures of relevance to this study. Regression analyses with bootstrapping were conducted to examine the hypothesized relationships including mediation. Results identified that an indirect only mediation model (full mediation) was present, such that greater facility capacity translates into increased facility usage through human resource capacity and program capacity. Therefore, recreation professionals and programs are indicated as pivotal to making the most of facility capacity.

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)0.002
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 score0.216
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.308
Teacher spread0.289 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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