Türkiye'de turizm amaçlı tahsis edilen ve kullanılan orman arazilerinin "sürdürülebilirlik" sorunu.
Bibliographic record
Abstract
Turkey is one of the leading tourism countries of the world. Tourism contributes to not only national economy but also regional development. Turkey has adhered to several international conventions regarding economic, socio-cultural and environmental sustainability. Nonetheless, since the onset of the 1980s, Tourism Encouragement Law’s main policies, along with the globalization and privatization, have developed mass tourism in Turkey, and led to continuous damage on the natural environment. Over the last thirty years, forest lands along the Mediterranean and Aegean coasts have been eradicated and over-exploited to a greater degree through the development of large-scale, inward-oriented and exclusive tourism investments, and second-home developments. This thesis investigates the extent to which forest lands in Turkey are allocated regarding ‘sustainability’ measures. It first makes a literature review on the notions of ‘sustainability’, ‘sustainable development’, ‘sustainable forest management’ and ‘sustainable tourism planning’, and examines institutional, stakeholder, policy and legal dimensions of tourism planning on forest lands in Canada and Australia, widely accepted with their advanced practices in the world to draw a theoretical framework and identify main components of ‘sustainability’. Second, it analyzes how far institutional, stakeholder, policy and legal structures in Turkey have accommodated the sustainability approach, while allocating forest lands to tourism. Then, it examines the recent development story of Belek Tourism Center (BTC) in Antalya by assessing ‘economic’, ‘socio-cultural’ and ‘environmental’ sustainability indicators. In the final part, the thesis underlines the major shortcomings and seeks to identify main policies for ‘sustainable’ allocation and use of forests for tourism in Turkey.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".