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Record W3026359152

Search (and rescue) for the ultimate selfie: How the use of social media and smartphone technology have affected human behaviour in outdoor recreation scenarios

2020· dissertation· en· W3026359152 on OpenAlexaboutno aff
Amy Harris

Bibliographic record

VenueSummit (Simon Fraser University) · 2020
Typedissertation
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsSelfieRecreationSocial mediaInternet privacySmartphone applicationAdvertisingApplied psychologyEngineeringPsychologyComputer scienceMultimediaWorld Wide WebBusinessPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The practice of outdoor recreation was historically a form of therapy and escape from the rigors of modern industrial daily work-life, and it remains a favored pastime today, with 70% of Canadians and 91% of British Columbia residents participating in “outdoor recreation or wilderness activities”. In recent years, there is a belief that the surge in popularity of hiking is due to beautiful destinations becoming more visible on social media. Further, the proximity of urban centres like Vancouver to such destinations reassures users that the safety benefits of urban technologies including smartphones, will remain accessible and reliable throughout their outdoor exploration and that help is available in the event of an emergency. This belief has led to many instances of Search and Rescue teams being activated, which would previously have been avoided by outdoor recreation participants making different choices based on their skill and experience. The culture of outdoor recreation has therefore been increasingly affected by smartphone technology in terms of users’ risk perception while recreating outdoors.

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)0.000
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 score0.424
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2020
Admission routes1
Has abstractyes

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