desarrolo ecoturistico para una red mundial de cooperacion entre las bahias mas bellas del mundo, dos casos comparativos :
Bibliographic record
Abstract
This study first presents current trend in the world tourism, the repercussions of the old models of tourism and the tourism alternatives to promote a lasting development. By selecting two bays belonging to The Most Beautiful Bay of the World Club, specifically Tadoussac bay in Quebec, Canada and Banderas bay in Mexico, we will be able to distinguish biophysics, socio-economic, political-administrative and tourist elements that, in the turn, will enable us to contrast the negative and positive impacts of tourism on the bays. The methodology is based on a study of strengths, opportunities, weaknesses and threats that will enable identifying the priorities that should be undertaken in each bay in order to develop tourism without threatening their natural and cultural environment. Currently, the concept of "ecotourism" is being widely promoted, but it has not yet clearly defined. Therefore, it could pose either a threat or a benefit to natural resources (the protected areas, biosphere reserves, conservative parks) and human resources (local communities). By comparing two bays, we will be able to highlight the promotional strategies developed by some ecotourism association on the local and regional level. Cooperation amongst the different organizations of the international level could contribute to sound ecotourism management. We contend that The Most Beautiful Bays of the World Club couid serve as a vehicle to faster promotion through the exchange of experience of circuits that encourage conservation of the bays and the education of the participants, ensuring the protection of the protection of their territory based on the principles of sustainable development.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".