Transformation in the context of uncertainty and compounding effects: Insights from marginal environments in India and Bangladesh
Bibliographic record
Abstract
The TAPESTRY project explores how deliberate transformation may arise from 'below’ in marginal environments with high levels of uncertainty. TAPESTRY is short for ‘Transformation as Praxis: Exploring Socially Just and Transdisciplinary Pathways to Sustainability in Marginal Environments’. TAPESTRY focuses on three ‘patches of transformation’ in India and Bangladesh – vulnerable coastal areas of Mumbai, the Sundarbans and Kutch which are experiencing diverse uncertainties emanating from climate change as well as anthropogenic factors including neoliberal urban development, economic growth and aggressive infrastructure development. The project focused on existing and emergent transformative alliances and asked how we can seek and support socially just and ecologically sound alternatives based on local people’s plural understandings of what transformation entails. What kind of hybrid alliances are emerging to facilitate these transformative processes in these locations? And what are the possibilities for scaling up and out of the positive learnings from these patches? A key conceptual innovation across all three patches was to think of transformation as praxis , by putting bottom-up change and the agency of marginalised people at the centre highlighting the practices and pathways of emergent changes and their barriers. In doing so, we address commonalities and differences across the three patches. A fragile coastline, shrinking and increasingly exploited mangrove forests, increasing exposure to climate hazards (such as cyclones, coastal erosion, flooding, sea level rise and extreme precipitation events), and diverse threats to marginal people’s livelihoods are the commonly observed factors. In terms of difference, we specifically focus on islanders in the transboundary Sundarbans forests (across the Bengal Delta in eastern India and Bangladesh), coastal fishing communities in the metropolitan region of Mumbai, and dryland pastoralists in Kutch in western India. Using a transdisciplinary approach, a central focus is on exploring pathways to transformation through a bottom-up approach using participatory methods including stakeholder roundtables, photovoice, and mixed methods. Through local and regional collaborations, we attempted to co-produce hybrid knowledge combining Indigenous understandings of ecosystem changes and climate impacts with science-based scenarios. The aim was to restore resource-based livelihoods by showcasing local community perspectives in local-level environmental governance.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".