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Record W2420867338 · doi:10.15402/esj.v1i2.98

Community-Based Action Research in Vancouver Public Schools: Improving the Quality of Children’s Lives through Secure and Sustainable School Food Systems and Experiential Learning

2016· article· en· W2420867338 on OpenAlexvenueaboutno aff
Alejandro Rojas, Elena Orrego, Stephanie Shulhan

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersUniversiti Malaysia Sarawak
KeywordsExperiential learningFood systemsInterviewQuality (philosophy)Key (lock)Action (physics)Action researchPublic relationsProcess (computing)Sustainable communitySociologyFood securitySustainable developmentPedagogyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The “key players in a community-based action research project in Vancouver Public Schools study” is a part of the Think&EatGreen@School (TEGS) project that aims to document the experiences of important actors within the movement towards healthy and sustainable Vancouver public school food systems and related learning opportunities. By interviewing key players in the Vancouver school food movement, we found that meaningful collaboration is a critical component in creating rich learning experiences that result in a more holistic and integrated perspective on food systems and improved quality of life. The TEGS Project is guided by principles of community-based action research (CBAR), an iterative process using community-university collaboration to identify opportunities, generate knowledge, and devise and implement locally-appropriate action to create desired change. Capturing the stories and experiences of key players represents an important step in articulating the learning emerging from this collaboration. Key Players commented on important networks, challenges, “success stories,” and styles of leadership that facilitate successes. The Key Players study has assisted the Think&EatGreen@School community of learners to better understand practices that constitute ‘ seeds of change,’ to use these seeds to replicate positive actions, and to refine and strengthen the direction of the project.

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)0.862
metaresearch head score (Gemma)0.614
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8620.614
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.5100.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.677
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 head0.316
GPT teacher head0.497
Teacher spread0.181 · 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; both teacher heads agree on what is shown here.

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
Published2016
Admission routes2
Has abstractyes

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