Indigenous food sovereignty and tourism: the Chakra Route in the Amazon region of Ecuador
Bibliographic record
Abstract
This research applies the concept of food sovereignty as a framework to explore the impacts of tourism on Indigenous food systems in the Chakra Chocolate and Tourism Route (referred to as the “Chakra Route” in the paper), a tourist destination in the Amazon region of Ecuador that aims to improve the livelihoods of Kichwa people. Using a qualitative and collaborative research approach, we examine how Kichwa and non-Kichwa people in this destination area understand food sovereignty, particularly concerning tourism development. Findings show that chakra gardens, a traditional agroforestry method, offer a symbolic and practical embodiment of food sovereignty for local people. Participants expressed a variety of values and concerns regarding tourism and chakra, including on destination branding; the role Indigenous women and their traditional knowledge play in tourism; the food choices promoted to tourists; self-determination and the level of participation of Indigenous people in governance of the route. Overall, our research contributes to a pluralistic notion of justice in Indigenous tourism and illustrates how the study of food sovereignty in this Amazonia destination can serve as a holistic and collaborative frame for exploring the multidimensional impacts of tourism on communal well-being, food security, and biodiversity and cultural conservation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".