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Record W3207998228 · doi:10.3389/fsufs.2021.737434

Centering Equity in Sustainable Food Systems Education

2021· article· en· W3207998228 on OpenAlexaff
Eleanor J. Sterling, Erin Betley, Selena Ahmed, Sharon Akabas, Daniel J. Clegg, Shauna Downs, Betty T. Izumi, Pamela Koch, Sara M. Kross, Karen Spiller, Lemir Teron, Will Valley

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of British Columbia
FundersDivision of Undergraduate EducationNational Science FoundationPharmaviteU.S. Department of AgricultureNational Institute of Food and AgricultureChapman Perelman Foundation
KeywordsOppressionEquity (law)Food systemsPublic relationsSociologyPolitical scienceEconomic growthAgricultureFood securityEconomicsGeography

Abstract

fetched live from OpenAlex

Sustainable food systems education (SFSE) is rapidly advancing to meet the need for developing future professionals who are capable of effective decision-making regarding agriculture, food, nutrition, consumption, and waste in a complex world. Equity, particularly racial equity and its intersectional links with other inequities, should play a central role in efforts to advance SFSE given the harmful social and environmental externalities of food systems and ongoing oppression and systemic inequities such as lack of food access faced by racialized and/or marginalized populations. However, few institutional and intra-disciplinary resources exist on how to engage students in discussion about equity and related topics in SFSE. We present perspectives based on our multi-institutional collaborations to develop and apply pedagogical materials that center equity while building students' skills in systems thinking, critical reflection, and affective engagement. Examples are provided of how to develop undergraduate and graduate sustainable food systems curricula that embrace complexity and recognize the affective layers, or underlying experiences of feelings and emotions, when engaging with topics of equity, justice, oppression, and privilege.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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