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Record W2505312762 · doi:10.1079/9781780644608.0203

Two Canadian mountaineering camps: participant motivations and sense of place in a wilderness setting.

2016· book-chapter· en· W2505312762 on OpenAlexaffabout
Robin Reid, Terry Palechuk

Bibliographic record

VenueCABI eBooks · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsMountaineeringWildernessAdventureOutdoor educationClimbingSense of placeGeographyRecreationPlace attachmentTourismArchaeologyPsychologyHistorySocial psychologyPolitical scienceEcologyPedagogyArt history

Abstract

fetched live from OpenAlex

This chapter describes the historical foundation, participant motivations and value of camps within alpine regions of Canada. The study involved participants at two different annual camp experiences - one with a long history of over 100 years providing mountaineering camps to approximately 30 guests in remote regions within the Canadian Rockies, and a second camp that started less than 5 years ago offering similar experiences to smaller groups of around 12 guests. In both instances, participants are flown into remote locations by helicopter, and experience week-long adventures in high mountain glacial regions. A key focus of the chapter is to explore the motivations and experiences related to sense of place in the mountain wilderness and the attachments and impressions participants have with and of the camps and the high alpine areas.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.006
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.029
GPT teacher head0.303
Teacher spread0.274 · 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 designQualitative
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

Citations0
Published2016
Admission routes2
Has abstractyes

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