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Record W2095663059 · doi:10.1089/eco.2010.0002

The Ecology of Adventure Therapy: An Integral Systems Approach to Therapeutic Change

2010· article· en· W2095663059 on OpenAlexaff
Duncan M. Taylor, David J. Segal, Nevin J. Harper

Bibliographic record

VenueEcopsychology · 2010
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAdventureWildernessExperiential learningPsychologyAdventure educationEcologyEpistemologyPsychotherapistTheory of changeProcess (computing)SociologyComputer scienceArtificial intelligencePhilosophyPedagogy

Abstract

fetched live from OpenAlex

Abstract Currently, a fragmentation in ideas exists regarding understanding psychological wellness and preferred routes to healing. This is evident in current adventure therapy (AT) literature, where unique combinations of experiential learning, challenge activities, novel experiences, group work, and other psychological theories are often used to account for positive outcomes and to explain mechanisms for change. Rarely is contact with wilderness environments included as an important variable associated with positive outcomes and change. AT has been rightly criticized for not recognizing the ecological paradigm of therapy conducted in wild nature. By including principles from integral systems theory, we offer adventure therapists a map, allowing for these seemingly disparate parts to fit together into a coherent whole. In addition, we propose that wilderness is a crucial cofacilitator in the change process. If seriously considered, these ideas pose a number of important questions for AT theory and practice.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.028
Scholarly communication0.0080.007
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.053
GPT teacher head0.377
Teacher spread0.324 · 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 designTheoretical or conceptual
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

Citations38
Published2010
Admission routes1
Has abstractyes

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