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Record W3043591162 · doi:10.15353/cfs-rcea.v7i1.332

The Value in Community Gardens: A Return on Investment Analysis

2020· article· en· W3043591162 on OpenAlexaffvenueabout
Susie Cochran, Leia Minaker

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInvestment (military)AgricultureReturn on investmentGovernment (linguistics)Value (mathematics)BusinessUrban agricultureAgricultural economicsEnvironmental resource managementGeographyProduction (economics)Environmental planningNatural resource economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Food production in cities is increasingly regarded as one of the building blocks for sustainable urban living, particularly as the agricultural industry faces mounting ecological and economic constraints, and populations continue to concentrate in urban centers. While substantial research exists on the qualitative outcomes of urban agriculture (UA), few studies present these outcomes in monetary terms that align with municipal decision makers economic priorities. In response to this gap, this paper reviews the literature on potential outcomes of one form of UA—community gardens—and identifies where gaps exist which prevent the translation of garden outputs into an economic quantity. The paper then describes a pilot return on investment assessment of a community garden in Ontario, Canada. Substantial data constraints were encountered, however the limited available data indicate that community gardens may represent an ROI-positive government investment. Further local-level research quantifying garden impacts would enable a more robust case for community gardens in cities.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.054
GPT teacher head0.224
Teacher spread0.170 · 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 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

Citations4
Published2020
Admission routes3
Has abstractyes

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