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Record W4241483696 · doi:10.24124/2002/bpgub1231

Experiential environmental education: a tool for mountain conservation in the Ecuadorian Andes

2002· dissertation· en· W4241483696 on OpenAlexaff
Kimberley Anne Horrocks

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsExperiential learningEnvironmental educationThreatened speciesContext (archaeology)AdventureGeographyMultidisciplinary approachOutdoor educationNational parkConvention on Biological DiversityConventionRecreationExperiential educationEnvironmental resource managementEnvironmental planningEnvironmental protectionBiodiversityPolitical scienceSociologyEcologyArchaeologyHabitatPedagogySocial scienceHistoryEnvironmental science

Abstract

fetched live from OpenAlex

The 1992 United Nations Convention on Biological Diversity recognizes mountain environments and mountain peoples as threatened. Multidisciplinary and interdisciplinary approaches have been identified as the recommended view in addressing associated environmental conservation issues. However, the knowledge that is generated or acquired by the study of mountain issues must be shared. Only once this sharing of knowledge has occurred can conservation and development schemes be effectively and collectively employed. This non-thesis project is a proposal for the knowledge sharing of mountain issues in the Andes of Ecuador. The proposed project is destined for urban youth between the ages of 14 and 17 years old, inhabitants of Quito, Ecuador. The method of delivery is through an experiential environmental education project that allows participants the use of all their senses in the discovery of the Andean environment.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.016
GPT teacher head0.336
Teacher spread0.320 · 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
Published2002
Admission routes1
Has abstractyes

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