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Record W2002886697 · doi:10.1177/1476750307083711

e-PAR

2008· article· en· W2002886697 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAction Research · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of TorontoYork University
FundersHealth Canada
KeywordsFacilitatorParticipatory action researchPromotion (chess)Health promotionAction researchPublic relationsSociologyCitizen journalismFocus groupCommunity-based participatory researchPsychologyPolitical sciencePedagogySocial psychologyNursingMedicinePublic health

Abstract

fetched live from OpenAlex

There is increasing interest in `moving upstream' in youth health promotion efforts to focus on building youth self-esteem, self-efficacy and civic engagement. Participatory Action Research (PAR) can be a powerful mechanism for galvanizing youth to become active agents of this change. Engaging youth in PAR and health promotion, however, is not always an easy task. This article describes a model (e-PAR) for using technology and Participatory Action Research to engage youth in community health promotion. The e-PAR Model was developed iteratively in collaboration with 57 youth and five community partners through seven projects. The Model is designed to be used with a group of youth working with a facilitator within a youth-serving organization. In addition to outlining the theoretical basis of the e-PAR Model, this article provides an overview of how the Model was developed along with implications for practice and research.

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.

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)0.010
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.979
GPT teacher head0.825
Teacher spread0.154 · 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