MétaCan
Menu
Back to cohort
Record W2512436733 · doi:10.1080/14733285.2016.1221058

Connecting to food: cultivating children in the school garden

2016· article· en· W2512436733 on OpenAlexfundaboutno aff
Kate Cairns

Bibliographic record

VenueChildren s Geographies · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNarrativeFutures contractNewspaperSociologyConsumption (sociology)Stewardship (theology)SustainabilityGender studiesPolitical scienceSocial scienceMedia studiesPoliticsEcologyArt

Abstract

fetched live from OpenAlex

School gardens are widely celebrated as spaces to promote health and sustainability by connecting children with their food. While scholars have assessed the effects of gardening in practice, media discourses play a key role in constituting this site. This paper examines how the school garden is discursively constituted within American and Canadian newspaper coverage. The analysis reveals specific forms of connection that are said to flourish in the school garden: between food production and consumption, between bodies and knowledge, and between the urban child and nature. While all children are said to benefit from connecting with their food, these connections are articulated differently in relation to particular bodies and spaces, evident in racialized and classed narratives of stewardship and salvation. As children’s relationship to food is invested with the hopes and fears of collective futures, the discursive construction of the school garden provides crucial insights into contemporary understandings of childhood.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.014
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.195
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations47
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueChildren s GeographiesSame topicUrban Agriculture and SustainabilityFrench-language works237,207