MétaCan
Menu
Back to cohort
Record W4403304355 · doi:10.1177/10538259241288470

Journeys to Far-Away Places: Wrestling with the Sustainability of Outdoor Education in Higher Education

2024· article· en· W4403304355 on OpenAlexaff
Morten Asfeldt, Simon Beames, Jannicke Høyem, Chris North, Takako Takano

Bibliographic record

VenueJournal of Experiential Education · 2024
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOutdoor educationAdventure educationEnvironmental educationSustainabilityExperiential educationPedagogySociologyPsychologyExperiential learningEcology

Abstract

fetched live from OpenAlex

Background: Over 35 years ago, the Brundtland Report entitled Our Common Future shed light on the impacts of human behavior on the environment and raised global awareness about sustainability. Since then, sustainability has become a prominent issue influencing most spheres of life including outdoor education (OE). Purpose: This inquiry seeks to identify the necessary conditions for justifying OE travel to far-away places. Methodology: We investigated this question using practitioner inquiry and thematic analysis. Findings: The authors believe some far-away journeys can be justified and recommend that these journeys are guided by featuring learning objectives that are best learned in specific far-away places; maintaining strong connections to the students’ everyday lives; including high levels of student involvement in sustainability-related decisions; and through providing novel and unexpected experiences that are unlikely to occur locally. Finally, we believe there is a case to be made for longer rather than shorter journeys. Implications: We caution against developing a binary which pits local OE against journeys to far-away places. Rather, both local and far-away journeys have an important role to play in OE programs that aim to build a world that is more socially and environmentally just.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.385
Teacher spread0.368 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experiential EducationSame topicOutdoor and Experiential EducationFrench-language works237,207