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Record W4236304784 · doi:10.24124/2019/59012

Wilderness perceptions of visitors to the Muskwa-Kechika Management Area, British Columbia

2019· dissertation· en· W4236304784 on OpenAlexaboutno aff
Odinn Steinsson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsWildernessSolitudeGeographyPerceptionResource (disambiguation)Wilderness areaEnvironmental resource managementEnvironmental planningPsychologyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

The Muskwa-Kechika Management Area (MK) in northeastern British Columbia, Canada is approximately 6.4 million hectares in size, with about 27% in parks and protected areas. I conducted and analyzed qualitative in-depth interviews, with the focus on the social perception of wilderness amongst users of the MK. Results indicate that users feel the MK is a prime wilderness area with exceptional opportunities to experience wilderness attributes such as solitude, both on the periphery and in the interior of the MK. To experience peace and quiet is one of the primary reasons for a visit to the MK. Where resource activity is encountered is critical to the wilderness experience. The closer a development is to the highway or in the beginning of a trip is more acceptable. Findings from this study can assist organizations such as the MK Advisory Board in identifying how specific resource development proposals might impact various wilderness 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)0.000
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 score0.138
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.283
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
Published2019
Admission routes1
Has abstractyes

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