Large-scale links between tourism enterprises and sustainable development
Bibliographic record
Abstract
The tourism sector is embedded within the global economy, human society and natural environment, and large-scale links outside the sector itself are as significant for sustainable development as small-scale environmental management practices within the industry. Sustainable development is still a somewhat contested term in the theoretical literature, but its practical interpretation by national governments and multinational corporations may be deduced from topics covered at the 1992 Earth Summit and the 2002 Rio World Summit on Sustainable Development (“Rio +10”) in Johannesburg. Agenda 21, the top-level policy product from the Earth Summit, did not include tourism in its sectoral studies, and a separate Tourism Agenda 21 was produced subsequently by the industry itself. Rio +10 did indeed include tourism, and a number of previous meetings such as the World Ecotourism Summit in Quebec produced internationally-agreed documents which were delivered at Rio +10. In addition to inputs from the tourism industry itself, tourism received a significant mention in contemporaneous inputs from the conservation sector, such as the Benefits Beyond Boundaries statement from the 2003 World Parks Congress. More recently, as governments worldwide have begun to grapple with policies related to climate change, the tourism sector produced a report and a declaration on this topic at Davos in 2007 (UNWTO et al., 2007).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".