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Record W4413740200 · doi:10.3390/youth5030089

From Strategy to Impact: How Young People Create Social and Environmental Change Through Youth Service Programs

2025· article· en· W4413740200 on OpenAlexafffundabout
Ilona Dougherty, Heather L. Lawford, Valentina Castillo Cifuentes, Amelia Clarke, Odeeth Lara-Morales, Aleksandra Spasevski

Bibliographic record

VenueYouth · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsBishop's UniversityUniversity of Waterloo
FundersUniversity of WaterlooCanadian Wildlife Federation
KeywordsService (business)Social changeEnvironmental changeBusinessEnvironmental planningPublic relationsEconomic growthPolitical scienceClimate changeMarketingGeographyEconomics

Abstract

fetched live from OpenAlex

Young people have a desire to meaningfully contribute to their communities and create lasting impact. While youth service programs aim to support this goal, research often emphasizes youth development over social and environmental outcomes. This study addresses this gap by analyzing six youth service programs run by three national Canadian non-profits. Using a youth-led social framework, we examine the impact strategies young participants employed to implement service projects. Our findings highlight how youth use diverse strategies to achieve social and environmental outcomes, and we propose adjustments to the existing framework to better capture youth contributions. This research broadens the understanding of youth impact, emphasizing that young people are not only beneficiaries of service but also agents of meaningful change.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.852

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.317
Teacher spread0.244 · 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
Published2025
Admission routes3
Has abstractyes

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