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Record W2936158731 · doi:10.33596/coll.34

School Travel Planning with the Kahnawake Schools Diabetes Prevention Project: Partnership Perspectives

2019· article· en· W2936158731 on OpenAlexaffabout
Soultana Macridis, Enrique Garcíá Bengoechea, Judith Ohsennenawi Jacobs, Alex M. McComber, Ann C. Macaulay

Bibliographic record

VenueCollaborations A Journal of Community-Based Research and Practice · 2019
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMcGill UniversityKahnawake Schools Diabetes Prevention ProjectUniversity of Alberta
Fundersnot available
KeywordsIndigenousParticipatory planningContext (archaeology)StakeholderGeneral partnershipPublic relationsParticipatory action researchCommunity-based participatory researchCommunity engagementStakeholder engagementSociologyEnvironmental planningPolitical scienceGeography

Abstract

fetched live from OpenAlex

Despite several studies examining the impact of school active transportation programming, there is limited understanding about undertaking the process of planning and evaluation from the perspective of community stakeholders. Most importantly, programming is rarely undertaken within an Indigenous context, which requires understanding of unique characteristics, culture, and needs. This study combined community-based participatory research with methods of ethnography within the Kahnawake Schools Diabetes Prevention Project in the Indigenous community of Kahnawake, Canada. This study fully engaged community members, built on pre-existing community and researcher strengths and increased the knowledge and understanding of active transportation to support schools in programming and implementation. In particular, this study, which may be relevant to other Indigenous and non-Indigenous communities, shed light on stakeholder perspectives of undertaking school active transportation program planning, which can inform the practice and provide support to others currently or planning to undertake similar projects.

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)0.025
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.008
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 head0.365
GPT teacher head0.548
Teacher spread0.183 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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