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Record W4388023639 · doi:10.1177/14767503231205238

The experience of youth-participatory action research in a social innovation lab: A methodological and organizational approach

2023· article· en· W4388023639 on OpenAlexafffundabout
Eugenia Canas, Richard Booth, Romaisa Pervez, Alec Cook, Melissa Taylor-Gates, Abe Oudshoorn, Ross Norman, Renée Hunt, Arlene G. MacDougall

Bibliographic record

VenueAction Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsLawson Health Research InstituteWestern University
FundersOntario Trillium Foundation
KeywordsParticipatory action researchAction researchSociologyCitizen journalismAction (physics)InstitutionQuality (philosophy)Social innovationPublic relationsPedagogyPolitical scienceSocial scienceEpistemology

Abstract

fetched live from OpenAlex

Based on the theory and quality criteria of Youth-Participatory Action Research (Y-PAR), youth and adult co-researchers at a social innovation lab in Ontario, Canada, have undertaken various knowledge generation and action activities for the purpose of supporting youth mental health and wellbeing among transitional-age youth (ages 16–25). We describe the methodological and organizational approach employed in this undertaking, including aspects of the social innovation model to support the action components of Y-PAR. We draw on Bradbury-Huang’s (2010) seven choice points for quality in action research to structure this collective reflection. Our experiences illustrate the tensions and opportunities arising from housing a Y-PAR project within a large health services institution. We also note how social innovation lab processes can support the emancipatory aims of participatory research. Implications for using Y-PAR in other areas are included.

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)0.122
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.644

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1220.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0320.071
Scholarly communication0.0170.010
Open science0.0050.032
Research integrity0.0050.009
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 head0.991
GPT teacher head0.844
Teacher spread0.147 · 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.

Study designQualitative
DomainMethods
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 routes3
Has abstractyes

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