Knowledge System Dynamics Andean Native Potato Production
Bibliographic record
Abstract
Background: Indigenous Knowledge Systems of rural Campesino communities in the Peruvian Andes are critical for ongoing maintenance and conservation of local food systems, supporting communities’ food and nutrition security while stewarding native potato genetic diversity. These knowledge systems face multi-level, compounding threats of erosion and remain misunderstood and misrepresented in research. To respond to and address these threats, these Indigenous Knowledge Systems must be understood not as static, but as dynamic processes of production and reproduction. Methods: Original research was conducted with two Campesino communities in the Peruvian highlands, Huancachi and Quilcas, both recognized for continued use of the ancestral system of ‘turno’ cultivation, a communal land management strategy and system of sectoral fallowing, and their stewardship of native potato biodiversity. Data was collected using mixed-method Participatory Action Research. This study uses systems thinking to examine, map and understand dimensions of local knowledge system in both communities and how they underpin Turno cultivation, examining the relationship between knowledge systems and native potato conservation.Results & Conclusion: Results from this study suggest the integral role that land, culture and worldview play in sustaining and reproducing Indigenous Knowledge for Turno cultivation, as well as the role of external actors in shaping relationships between systems elements. This study found that as access and ownership over land is threatened, the power and influence of external actors over these local knowledge systems is increased, which in turn shapes relationships and functions of the knowledge system.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".