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Record W2336074960 · doi:10.1080/14927713.2016.1169436

Development of the psychologically deep experiences (PDE) in nature scale

2016· article· en· W2336074960 on OpenAlexaffvenue
Fenton Litwiller, Gordon J. Walker

Bibliographic record

VenueLeisure/Loisir · 2016
Typearticle
Languageen
FieldPsychology
TopicFlow Experience in Various Fields
Canadian institutionsUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsCommunitasConfirmatory factor analysisPsychologyScale (ratio)RecreationStructural equation modelingPerceptionSocial psychologyApplied psychologySociologyMathematicsCartographyGeographyAnthropologyStatistics

Abstract

fetched live from OpenAlex

Mannell described psychologically deep experiences (PDEs) as meaningful events that alter awareness of the passage of time, perceptions of self and the environment. This study integrated 4 PDEs – Communitas, Fascination, Flow experiences and Spiritual experiences by using 11 constructs, developed in preexisting scales, designed to measure them. Our purpose was to develop a valid and comprehensive, nature-based PDE scale. After an expert review (n = 5) and conducting interviews (n = 12), the newly developed scale was included in an online questionnaire composed of nature-based recreationists (n = 431). Confirmatory factor analysis indicated good overall fit for the test and cross validation samples (e.g. RMSEA = 0.047 and 0.038, NFI = 0.94 and 0.94). Although the Communitas and Fascination dimensions require empirical refinement, our findings help to distinguish among four correlated dimensions of meaningful outdoor recreation experiences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.018
GPT teacher head0.314
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2016
Admission routes2
Has abstractyes

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