A Mixed Methods Study on Community-Based Tourism as an Adaptive Response to Water Crisis in San Andrés Ixtlahuaca, Oaxaca, Mexico
Bibliographic record
Abstract
Water scarcity is a threat in San Andrés Ixtlahuaca, Mexico, that imperils the survival of farming households whose food and income depend on rainfed agriculture. This research extends the framework of socioecological systems to tourism to understand how community-based tourism flourishes: not spontaneously but as part of an adaptive response to the water crisis. A research model was constructed based on mixed methods. For the qualitative approach, interviews were conducted with 12 community leaders. Results show that different capabilities have been developed throughout the adaptive cycle: information capabilities at the Ω phase; involvement capabilities at the α phase; self-esteem capabilities at the r phase; and resource use capabilities at the k phase. These capabilities make it possible to face the water crisis, but they also favor the implementation of tourist activity. For the quantitative approach, a questionnaire was applied to 88 community participants directly involved in tourism activities to discover the current state of the tourism-related capabilities, their shaping, and relationships. A partial least squares structural equation modeling (PLS-SEM) to test the hypotheses raised was used. The activity makes the community resilient because it seeks to conserve and improve community resources through tourism-related capabilities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".