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Record W2971592456 · doi:10.3390/su11184916

Insights for Building Community Resilience from Prioritizing Youth in Environmental Change Research

2019· article· en· W2971592456 on OpenAlexafffundabout
Evan J. Andrews, Kiri Staples, Maureen G. Reed, Renee Carriere, Ingrid MacColl, Lily McKay-Carriere, Jennifer Fresque-Baxter, Toddi A. Steelman

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsGovernment of Northwest TerritoriesUniversity of SaskatchewanUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntervention (counseling)CreativityPsychological resiliencePsychological interventionPsychologyResilience (materials science)Applied psychologySocial psychology

Abstract

fetched live from OpenAlex

Youths are the next generation to foster community resilience in social–ecological systems. Yet, we have limited evidence on how to engage them effectively in learning about environmental change. One opportunity includes the involvement of youths in research that connects them with older generations who can share their values, experiences, and knowledge related to change. In this community-based study, we designed, assessed, and shared insights from two intergenerational engagement and learning interventions that involved youths in different phases of research in the Saskatchewan River Delta, Canada. For Intervention 1, we involved students as researchers who conducted video and audio recorded interviews with adults, including Elders, during a local festival. For Intervention 2, we involved students as research participants who reflected on audio and video clips that represented data collected in Intervention 1. We found that Intervention 1 was more effective because it connected youths directly with older generations in methods that accommodated creativity for youths and leveraged technology. Engaging the youths as researchers appears to be more effective than involving them as research participants.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.398
Teacher spread0.291 · 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 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

Citations6
Published2019
Admission routes3
Has abstractyes

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