Bibliographic record
Abstract
الملخص: تهدف هاته المداخلةالى عرض التجربة الكندية في مجال تطبيق اليقظة السياحية كأداة لتطوير قطاعها السياحي،من خلال التعرف على مزايا هذا الأسلوب التسييري الحديث وانعكاساته على السياحة الكندية بإعتبار أن السياحة اليوم أصبحت من أهم القطاعات التي تعرف تطورا كبيرا مقارنة بباقي القطاعات،وهو ما جعل التنافسية اكثر اشتدادا،ما ألزم الدول السياحية على البحث عن أساليب تسييرية تواجه بها التحديات وتستغل بها الفرص المحيطة بالبيئة السياحية التي تعيش فيها.حيث استطاعت السياحة الكندية من خلال تبني اليقظة السياحية العالمية من تحقيق قفزة نوعية في ما يخص استقطاب السياح الأجانب،وهو ما انعكس ايجابا على اقتصادها القومي الذي استطاع من خلق مناصب عمل جديدة وتدعيم عائداتها السياحية . \nAbstract : The theme of intervention the view of the Canadian experience in the field of tourism vigilance application as a tool to develop the tourism sector, by identify the advantages of this method modern and its implications for Canadian tourism, today tourism has become one of the most important sectors that defines a great development compared to other sectors, which What made more competitive , what tourist countries committed to search for methods of managerial facing the challenges and exploit the opportunities in tourism environment.Where Canadian tourism has been able to achieve development in terms of attracting foreign tourists, which is reflected positively on the CanadianEconomy, which could create new job positions and strengthen tourism revenues
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.301 | 0.261 |
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".