“Central” and “peripheral” adaptation pathways of entangled agrifood systems transformations
Bibliographic record
Abstract
In the agrifood systems of developing countries, local adaptation actions and pathways often interact with other climate and development responses, creating new trade-offs, uncertainties, and potentially maladaptive outcomes. While knowledge on the interacting pathways of adaptation is expanding, previous studies have focused on complex systems dynamics, and studies that address the human, social, and political forces that drive the cascading of risks between different coupled social-ecological systems are few. This paper aims to examine climate adaptation trade-offs, uncertainties, and maladaptation through an interdisciplinary analysis of two interacting pathways of transformational adaptation in the Philippines: the post 2004 disaster rural transformations in the coconut-producing municipality of Infanta and a state-led urban water resilience strategy for the capital region of Metro Manila. Data were collected from January 2021 to March 2022 through ethnographic field visits, participant observation, focus group discussions, semi-structured interviews, geospatial analysis, multicriteria mapping sessions, and review of planning documents and secondary data sources. Key findings suggest that the pathways of transformation and their entanglement are rooted in historical processes of change and that maladaptation is contingent on the political relations between the “central” and “peripheral” pathways. Overall, the paper offers a significant contribution to adaptation research in the agrifood systems of developing countries as it calls for a deeper kind of collective reflexivity and action that can transform narrow notions and practices of resilience and sustainable development.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".