Towards Smart Trends for Tourism Development and its Role in the Place Sustainability- Karbala Region, a Case Study
Bibliographic record
Abstract
Tourism is currently considered one of the most important economic sectors that directly or indirectly affect societies and have a positive role in achieving spatial development, whether on (the country, regions, or cities) and its sustainability, and one of the modern trends in the field of achieving sustainability is adopting Elements of (intelligence) in development activities, the most important of which is smart tourism, which has the potential to make a qualitative and quantitative transition in the life of the local community in various aspects (economic, social, environmental, and environmental urban) if smart variables (smart governance - sustainability - technology - innovation - accessibility and smart mobility - communication and information technologies - social capital - cultural heritage - creativity) are taken more seriously in applying them to the reality of the situation in planning, implementation and management, allowing the investment of strengths and positive disks to overcome risks, challenges and weaknesses. This study was distinguished from the previous studies on the topics of smart tourism in that it relied on all indicators that contribute to the development of smart tourism in the field of sustainable spatial development by relying on the development potential of the regions, which greatly encourages the introduction of smart technology mainly in development. As is the case in the province (Karbala) in Iraq, which was chosen as a study area because of its great tourism development potential to find the available and latent opportunities in the transition to smart tourism, as well as to assess its role in sustainable development. The study reached important results through the use of statistical methods, including SPSS, represented in the presence of a strong correlation between smart tourism indicators and sustainable spatial development. Tourism supply and demand directly interact intimately, whose results are reflected on the entire Karbala region in the short and long term.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".