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Record W2980846471

Historical perspectives on the ties between cities and food

2019· article· en· W2980846471 on OpenAlexaboutno aff
Nicolas Bricas, Damien Conaré

Bibliographic record

VenueOpenEdition (OpenEdition) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic geographyGeography
DOInot available

Abstract

fetched live from OpenAlex

The 20th century marked a step change in how cities think of their food supply. In the preindustrial world, where cities grew organically, urban layouts were heavily shaped by food, as witnessed by the city center locations of sites such as markets and slaughterhouses. Hygiene policies and then the imperatives of food security in an urbanized world, gradually pushed food and farming out of the city entirely, engendering a progressive distancing between cities and their food. This distancing encompasses many forms, at once geographical, economic, cognitive and political. Some cities, such as Toronto, Canada and Belo Horizonte, Brazil have pioneered incremental reappropriation of food policies by a variety of urban actors. The revival of urban food policies extends well beyond questions of urban agriculture and food production. However, urban agriculture does have a role to play in this respect. The challenge is less about feeding cities – it is a form of farming with a limited production potential – than about reintroducing nature and agriculture into the heart of the city, while simultaneously rebuilding social ties. The symbolic dimension should not be underestimated.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0110.023
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.019
GPT teacher head0.196
Teacher spread0.176 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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