Indigenous women’s approaches to tourism planning: lessons from Ecuador
Bibliographic record
Abstract
In the intersection of Indigenous tourism and gender, a pressing question arises: How can Indigenous women lead sustainability and gender equality in the tourism industry? To shed light on this issue, we conducted qualitative and case study research in the Amazonia region of Ecuador. Our study analysed the roles that Napo Runa women play in tourism development, their efforts to align tourism with their sustainability and gender equality goals, and the factors that shape these efforts. Our findings highlight the importance of focusing on the relationships that surround Indigenous women and recognizing the various forms of discrimination they face. We also found that Indigenous planning, led by Indigenous women, is a crucial tool for promoting more visible and empowered roles for Indigenous women in the tourism sector. The case study supports the argument that addressing gender inequality is a crucial first step toward sustainable business practices, especially in cases where women’s economic participation is invisible and influenced by violence and gender discrimination. This research contributes to the work of scholars and practitioners aiming to advance sustainability and justice in the tourism industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".