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Record W4200453673 · doi:10.3390/su132413562

What Can We Learn from Rural Youth in British Columbia, Canada? Environment and Climate Change—Issues and Solutions

2021· article· en· W4200453673 on OpenAlexaffabout
Pranita Bhushan Udas, Bonnie Fournier, Tracy Christianson, Shannon Desbiens

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsPhotovoiceClimate changeStewardship (theology)Participatory action researchEnvironmental stewardshipPositive Youth DevelopmentPolitical scienceAction researchEnvironmental planningGeographyEnvironmental resource managementSociologyEconomic growthEcologyEnvironmental science

Abstract

fetched live from OpenAlex

“What can we learn from rural youth?” was a youth-led arts-based participatory action research project carried out to understand and facilitate positive youth development in two rural communities in the province of British Columbia, Canada. Data was collected using photovoice, visual art, journal reflections, and group discussions. During the study, youth expressed a strong connection with nature for their development or wellbeing. Issues such as environmental degradation and climate change were identified as causes for concern. They discussed human responsibility for environmental stewardship both in their local communities and globally. Climate change hazards such as flood and fire, human action leading to environmental pollution, and human responsibility for environmental stewardship surfaced as issues for their development. Youth expressed a felt responsibility to act on climate change and to reduce the anthropogenic impact on the Earth. Based on youth voices, we conclude that attempts to engage youth in climate action without considering their psychosocial wellbeing, may overburden them.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.874

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.276
Teacher spread0.261 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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