Nurturing Socio-ecological Spaces Through Urban Gardening Practices in India: An Exploration of Alternate Imaginations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".