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Record W2903177901 · doi:10.5539/jsd.v11n6p111

Urban Gardening in Florence and Prato: How a Science Shop Project Proposed by Citizens Has Grown into a Multi-Disciplinary Research Subject

2018· article· en· W2903177901 on OpenAlexvenueno aff
Franco Bagnoli, Ada Baldi, Ugo Bardi, Marina Clauser, Anna Lenzi, Simone Orlandini, Giovanna Pacini

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
FundersUniversità degli Studi di Firenze
KeywordsSubject (documents)DisciplineUrban agricultureSubsistence agricultureRelevance (law)AgriculturePoliticsSociologySocial sciencePolitical scienceGeographyArchaeologyLibrary scienceLaw

Abstract

fetched live from OpenAlex

Urban gardening mainly means growing edible vegetables in a town. This practice has been traditionally used for economic reasons (subsistence agriculture), but now it has also acquired educational, nutraceutical, therapeutic and social relevance. The educational aspect of urban gardening has been the subject of a proposal for the newly born Science Shop in Florence (Italy). In the spirit of action-research, in our project we first decided to involve all (or many) potentially interested people. This has brought into light the galaxy of different aspects related to urban gardening and allowed the establishing of promising research lines. We discovered that this is a multi-disciplinary subject that touches themes dealing with agriculture, botany, psychology, chemistry, city planning and politics. We examine here the various aspects of urban gardening in the towns of Florence and Prato, two very different urban environments despite their proximity.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.014
Scholarly communication0.0060.003
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.053
GPT teacher head0.298
Teacher spread0.245 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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