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Record W4322493475 · doi:10.54656/jces.v15i2.456

Community Engagement and Giving Back among North American Indigenous Youth

2023· article· en· W4322493475 on OpenAlexaboutno aff
Michelle L. Estes, Kelley J. Sittner, Kyle X. Hill, Miigis B. Gonzalez, Tina Handeland

Bibliographic record

VenueJournal of Community Engagement and Scholarship · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on Drug AbuseNational Institute of Mental Health
KeywordsIndigenousCommunity engagementViewpointsConceptualizationGeneral partnershipPublic relationsParticipatory action researchSociologyCitizen journalismCommunity-based participatory researchPsychological resiliencePolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

"Volunteer participation" refers to free engagement in activities that benefit someone or something else. Volunteering can produce many benefits for individuals and communities. However, current research examining volunteer participation often excludes diverse viewpoints on what constitutes volunteering, particularly the perspectives of North American Indigenous youth. This oversight may result from researchers' conceptualization and measurement of volunteering from a Western perspective. Utilizing data from the Healing Pathways (HP) project, a longitudinal, community-based participatory study in partnership with eight Indigenous communities in the United States and Canada, we provide a detailed description of volunteer participation and community and cultural engagement. Overall, we employ a community cultural wealth lens to emphasize the various strengths and sources of resilience that these communities possess. At the same time, we encourage scholars and the wider society to broaden their views of volunteering, community involvement, and giving back.

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)0.026
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.004
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 head0.206
GPT teacher head0.341
Teacher spread0.136 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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