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Record W4414374264 · doi:10.1177/24557471251371533

Nurturing Socio-ecological Spaces Through Urban Gardening Practices in India: An Exploration of Alternate Imaginations

2025· article· en· W4414374264 on OpenAlexaff
Deborah Dutta, Amrita Hazra

Bibliographic record

VenueUrbanisation · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConsumption (sociology)Meaning (existential)Space (punctuation)Nature versus nurturePoliticsSituatedEthnographyCraftQualitative research

Abstract

fetched live from OpenAlex

Due to largely opaque processes of consumption and production in urbanised spaces, the dependence of humans on food ecosystems is largely invisibilised. To counter this challenge, a growing awareness about industrial food production and consumption patterns along with a need to create alternatives has given rise to a range of small-scale food-growing initiatives in urban areas. This study focuses on urban spaces in seven Indian cities to examine how alternative, localised practices—such as urban gardening—can foster civic participation and promote relational well-being. Based on a qualitative study involving growers’ narratives, we argue that urban gardening can nurture a generative space to meaningfully engage with the local socio-ecological systems. Drawing on Soja’s concept of ‘Thirdspace’, the study explores ways in which the gardening space serves as a hybrid site embedding growers’ evolving imagination and negotiated meaning of cultivated spaces. The study characterises ways in which community gardening can be an educational, social practice to bridge personal motivations with political commitments aligned with ecological sustainability.

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 score0.136
Threshold uncertainty score0.203

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 studies0.0000.000
Scholarly communication0.0000.002
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 head0.044
GPT teacher head0.301
Teacher spread0.256 · 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
Published2025
Admission routes1
Has abstractyes

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