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Record W1993606364 · doi:10.1080/14927713.2014.906176

Promoting sustainable food and food citizenship through an adult education leisure experience

2013· article· en· W1993606364 on OpenAlexafffundvenue
Alan Warner, Edith Callaghan, Cate de Vreede

Bibliographic record

VenueLeisure/Loisir · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsAcadia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCitizenshipParticipatory action researchCitizen journalismContext (archaeology)SociologyPublic relationsQualitative researchReflective practiceAction researchParticipant observationPsychologyPedagogyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This action research project investigates a community-based, participatory learning approach to promoting sustainable food choices and food citizenship through a project-based leisure experience. Drawing on understandings from radical adult education, community-based social marketing and practice theory, a project-based leisure experience was designed, implemented and assessed. This experience involved volunteers hosting friends for a sustainable meal in their homes. The meal included guided activities, critical reflection on food system issues, values-based dialogue and written commitments to shift habits. A combination of participant observations, surveys and follow-up qualitative interviews indicated that the meal program had an influence on those involved, shifting habits and increasing sustainable food choices. Changes seemed owing to increases in motivation that were anchored in reflection on personal values rather than on a reduction in external barriers. A synthesis of the empirical findings and literature suggests five key characteristics of an adult education approach to project-based leisure that can facilitate food citizenship: personal social context, engaged experiences, social norms, social networks and community-based resources.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.253
Teacher spread0.241 · 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

Citations14
Published2013
Admission routes3
Has abstractyes

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