Traditional food or biocultural threat? Concerns about the use of tilapia fish in Indigenous cuisine in the Amazonia of Ecuador
Bibliographic record
Abstract
Abstract This article contributes to streams of knowledge related to biocultural diversity, food tourism and the cultural impacts of introduced species. Specifically, it explores the concerns that arise from the promotion of tilapia fish Oreochromis niloticus in Indigenous cuisine along a touristic route in the Amazon region of Ecuador. Although the environmental impacts of tilapia in the Amazon ecosystem have been largely documented, reports of cultural impacts are still scarce. This research addresses this gap by applying a biocultural approach, which provides a more systemic and pluralistic view of this introduced species in the local food systems of this region. This qualitative research used semi‐structured interviews, observations, a workshop and the analysis of restaurant menus to understand the complexity of the tilapia issue in this case. The results report the factors influencing the promotion of tilapia fish as traditional food, how locals perceive this promotion and its impacts on local culture and biocultural conservation, and locals’ proposals to mitigate these impacts. The discussion section uses a biocultural ethics approach to analyse these results. We focus on stakeholders' perspectives and actions to address the tilapia issue in their region while navigating their multiple ways of valuing their human–environment relations and adapting to uncertain scenarios.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".