Buen Vivir: Degrowing extractivism and growing wellbeing through tourism
Bibliographic record
Abstract
Buen Vivir (BV) is a holistic vision for social and environmental wellbeing, which includes alternative economic activities to the neoliberal growth economy. This article looks at how tourism initiatives under a BV approach can lead to degrowth by drawing on a case study of how BV is put into practice through tourism in the Cotacachi County in Ecuador. We argue that by degrowing socially and environmentally damaging extractive sectors and growing alternative economic activities like community-based tourism, a BV approach could increase social and environmental wellbeing. We refer to LaTouche’s notion of degrowth as a matrix of multiple alternatives that will reopen the space for human creativity. This complements the notion of BV as a plural approach, and in turn works to decolonise the parameters of how we might understand degrowth. In the case of Cotacachi, the vision for tourism is based on the needs of the community, rather than to satisfy a Eurocentric ideal of development supported by a policy of extractivism. BV is key to how this community conceptualises the potentialities of tourism because it considers the wellbeing of the people and the environment. In this case, degrowth is a consequence of BV, rather than the objective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".