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Record W2740700724

Urban agricultural practices and initiatives in built environments: case studies of Detroit and Singapore explored

2016· article· en· W2740700724 on OpenAlexaboutno aff
Sudeshna Ghosh

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureEnvironmental planningGeographyEnvironmental resource managementRegional scienceArchaeologyEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

Global communities are recognising the importance of integrating urban food production locally and adopting to agrarian lifestyles in cities. In this paper, review and analysis on three selected world cities: City of Vancouver, New York, and Hong Kong are conducted considering important factors of urban characteristics and practices, initiatives and performances of urban agriculture and a set of criteria is formulated. A comparative analysis of two case studies of different density cities: Detroit and Singapore is undertaken in detail based on this criteria developed. Evolving out of varying contexts and processes that have shaped urban agricultural movements in these cities, this research offers a unique insight into the lives of these two cities. Urban agriculture plays vital roles in world cities in sustaining and creating liveable and productive places and building community resilience. Outcomes suggest that functional processes and appropriate and well-aligned policies and strategies on urban food production linked to urban planning policies can transform cities and can create curative places for wellbeing and improved food security of residents. Involvements of government, private organisations, local councils, and residents would be essential for a successful long-term continuation of these practices. Future research should focus to strengthen transdisciplinary connections between health, planning and other disciplines.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.237
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.227
Teacher spread0.200 · 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 teacher head, 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

Citations1
Published2016
Admission routes1
Has abstractyes

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