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Record W7056046472

Enhancing Student Psychological Well-Being in Secondary Outdoor Education

2024· article· en· W7056046472 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDeci-Outdoor educationNarrativeTheme (computing)PerceptionControl (management)Action (physics)Formative assessment
DOInot available

Abstract

fetched live from OpenAlex

This article reports on an examination of the perceptions of New Brunswick Outdoor Education (OE) teachers regarding the role and impact of various concepts of self and well-being in their high school OE classes. The researcher sought to analyze OE instructors’ observations and identify the pedagogy employed to enhance student well-being. A participatory action research (appreciate inquiry) approach was utilized. Data underwent narrative analysis and theme coding. While the emergent themes echoed the concepts in Deci & Ryan’s (2008) Self-Determination Theory (SDT), more significantly they demonstrated the importance of prioritizing certain SDT components over others throughout progression of the course. Findings also indicated that control over certain aspects of the course were necessary to manage to meet these prioritized needs, including relationships, selecting activities, creating a challenging environment, and the use of time within the course structure. Steps have already been untaken to actualize results within NB high schools.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.117
GPT teacher head0.533
Teacher spread0.416 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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