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Record W4412008551 · doi:10.3390/youth5030066

Applying the 7P Framework to Youth–Adult Partnerships in Climate Organizing Spaces: “If We Are Going to Be the Ones Living with Climate Change, We Should Have a Say”

2025· article· en· W4412008551 on OpenAlexafffund
Ellen Field, Lilian Barraclough

Bibliographic record

VenueYouth · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of GuelphLakehead University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsClimate changeEnvironmental resource managementPolitical scienceEnvironmental planningGeographyEnvironmental scienceOceanographyGeology

Abstract

fetched live from OpenAlex

Young people are frustrated and disheartened with the lack of adult leadership and action to address the climate crisis. Although youth representation in global, regional, and local decision-making contexts on climate change is steadily growing, the desired role and effect of youth in environmental and climate decision-making has shifted from a focus on having youth voices heard, to having a direct and meaningful impact on policy and action. To meaningfully integrate youth perspectives into climate policies and programs, intergenerational approaches and youth–adult partnerships are key. This paper explores strategies to support youth action and engagement as adult partners by investigating youth perspectives on what adults and adult-led organizations should consider when engaging young people in climate-related work. This qualitative research study introduces a revised version of the 7P youth participation framework, developed through focus groups with high school youth. This paper provides reflective questions and practical recommendations for participants engaged in youth–adult partnerships to help guide engagement beyond token representation and create meaningfully participatory conditions for youth agency in climate organizing spaces.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.139
GPT teacher head0.334
Teacher spread0.194 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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