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A Picture<i>and</i>1000 Words: Using Resident-Employed Photography to Understand Attachment to High Amenity Places

2004· article· en· W87470372 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Leisure Research · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsCanadian Forest ServiceUniversity of New Brunswick
Fundersnot available
KeywordsAmenityPhotographyPsychologyPlace attachmentSociologySocial psychologyVisual artsArtPolitical science

Abstract

fetched live from OpenAlex

AbstractResearch on attachment to high amenity places has usually focused on visitors, despite the fact that many of these settings also may hold permanent residents. Visitor employed photography (VEP) has been used to understand landscape elements that increase the quality of the recreational experience. Our research applies the techniques of VEP to analyze local elements that foster place attachment among permanent residents of high amenity areas. We provided single use cameras to 45 subjects in two communities located in and adjacent to Jasper National Park, Alberta, instructing them to take photos of elements that most attach them to their community. Our results reveal a complex relationship between ecological and sociocultural factors in attachment; these elements are not separate, but help define each other.KEYWORDS: Sense of placevisitor employed photography

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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.142
GPT teacher head0.457
Teacher spread0.316 · 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