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Record W2979645913 · doi:10.4324/9780429427138-13

What’s on the menu? Succulent sustainability goes to school

2019· book-chapter· en· W2979645913 on OpenAlexaboutno aff
M. A. McKenna, Jessica Wall, Suchitra Roy

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessEcologyBiology

Abstract

fetched live from OpenAlex

Schools are a fertile setting for succulent sustainability. Students can improve their plant-based food literacy and sustainability education while acquiring life and vocational skills. Although the term succulent sustainability is not used in Canada yet, many schools in all regions offer related programmes. This chapter identifies potential components of succulent sustainability; summarizes the status of programmes, policies, and curricula in Canada; and highlights examples of research and programmes. Canadian schools have increased their involvement in plant-based initiatives in a relatively short time. Research and programme examples indicate considerable progress in establishing school gardens, youth leadership programmes, and food sustainability education. Researchers note, however, that more work is needed to reach all students, to offer programmes that address all aspects of food literacy, and achieve systems change. Benefits to students are far-reaching and include increased awareness of food and willingness to try new foods, increased gardening and food preparation knowledge and skills, enhanced social skills, increased school pride, and improved social inclusion within schools. The chapter ends by emphasizing the need for partnerships, strategic planning, educational resources, funding and training, monitoring, and policies to close existing gaps. In sum, Canada requires action at all levels to enable all students and schools to reap the full benefits of succulent sustainability.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.028
GPT teacher head0.342
Teacher spread0.314 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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 routes1
Has abstractyes

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