Bibliographic record
Abstract
In 2002, the United Nations declared the International Year of Ecotourism, whose peak event was the World Ecotourism Summit, held in Quebec, Canada in May of that year. Ecotourism has since presented many formidable challenges including the following: many of the world’s natural areas remain under threat; there has been a further loss of biodiversity and resources for conservation remain inadequate; world tourism arrivals have grown by 23% and are forecast to double by 2020; climate change has increasingly become a major threat affecting the very resources on which ecotourism depends – natural areas and local and Indigenous communities around the world; this has helped raise awareness of the contribution of current and future tourism operations and services to global climate change; the role of tourism in supporting sustainable development and the achievement of the Millennium; development Goals, notably the alleviation of poverty, has become recognized as a critical industry responsibility; ecotourism has articulated the core principles of sustainability in the travel and tourism industry and therefore plays a leading role within the industry as a whole (The International Ecotourism Society, 2007). With this mind, this research article deals with problems and emerging Trends associated with the Australian ecotourism marketing and how they are impacting Australia’s tourism patterns and also future trends for Ecotourism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".