Safeguarding the land to secure food in the highlands of Peru: The case of Andean peasant producers
Bibliographic record
Abstract
Local or traditional agri-food systems in the Andes depend on community land use planning to maintain the genetic pool of crops and landraces in the face of disease, disasters, and climate change. These systems are managed integrally and on the basis of traditional knowledge around soil conservation, water management and maintaining biodiversity. At the same time, agri-food system research, policy and programming exhibit a limited understanding of local or traditional systems planning and community and cultural contexts. In policy and programming, the treatment of communities as homogenous groups overlooks heterogeneity in local identities, which is reflected for example in different access and use of traditional knowledge among men and women and forms of community organization and customs. The purpose of this article is to respond to this gap by shedding light on the intersecting identities of Andean farmers–peasant women and men–that contribute to the sustainability and resilience of local agri-food systems. Our focus is on intersecting identities and planning processes in particular. We detail the nature and cultural components that make up local agri-food systems in the Andean region and identify policy gaps around identities. To do this, we draw on intersectional feminist thinking, socio-ecological systems and resilience thinking to apply an intersectional lens to the study of planning processes in several Andean communities. Findings identify contributions around soil conservation, biodiversity upkeep, water management, and communal or cultural practices that are shaped by peasant's intersecting identities and their interactions within social-ecological systems. Findings illustrate the importance of multiple social locations, relations, and structures of power, including but not limited to gender, but other categories such as age and ethnicity for the delivery of equitable resilience. We formulate some initial recommendations so that national approaches and interventions better reflect the diversity of Andean people's identities and the way these affect relationships with socio-ecological systems in national and public planning. In particular, we suggest there may be value in exploring further the potential of rights-based approaches for enhancing equitable resilience in Andean agri-food systems. This article should be of interest to academics and practitioners in planning working around local or traditional food systems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".