Advances in global services and retail management: Volume 2
Bibliographic record
Abstract
This is the second volume of the Advances in Global Services and Retail Management Book Series. This volume has the following parts: Part 1: Hospitality and Tourism Part 2: Marketing, E-marketing, and Consumer Behavior Part 3: Management Part 4: Human Resources Management Part 5: Retail Management Part 6: Economics Part 7: Accounting and Finance Part 8: Sustainability and Environmental Issues Part 9: Information Technology ISBN: 978-1-955833-03-5 Hospitality and Tourism Significance of VR in the spa: A spatial analysis Irini Lai Fun Tang, Schultz Zhi Bin Xu, and Eric Chan Social media marketing in rural hospitality and tourism destination research Samuel Adeyinka-Ojo and Shamsul Kamariah Abdullah All aboard! Is space tourism still a fantasy or a reality: An investigation on Turkish market Emrah Tasarer, Vahit Oguz Kiper, Orhan Batman, and Oguz Turkay Strategic consciousness and business performance relationship of open innovation strategies in food and beverage businesses Muhsin Halis, Kazim Ozan Ozer, Hasan Cinnioglu, and Zafer Camlibel The effects of COVID-19 epidemic on guided tours and alternative tour samples from Turkey Bayram Akay The effect of COVID-19 phobia on holiday intention Halil Akmese and Ali Ilgaz The effect of the usage of virtual reality in tourism education on learning motivation Sarp Tahsin Kumlu and Emrah Ozkul The impact of effective implementation of customer relationship management to the success of hotels in Afikpo North local government of Ebonyi State, Nigeria Ogboagha Callister and Managwu Lilian The influence of study travel on quality-oriented education: The case of Handan, China Wang Jingya and Alaa Nimer Abukhalifeh The impact of U.S. Cuba policies on Cuban tourism industry: Focus on the Obama and Trump Administration Jukka M. Laitamaki, Antonio Diaz Medina, and Lisandra Torres Hechavarria Determination of students’ characteristics and perspectives about social entrepreneurship: A case of Anadolu University Muhammed Kavak, Ipek Itir Can, and Emre Ozan Aksoz The place of Kazakhstan tourism sector in the countries of the region in terms of transportation infrastructure Maiya Myrzabekova, Muhsin Halis, and Zafer Camlibel What are tour guides most praised for? A sharing economy perspective Derya Demirdelen-Alrawadieh and Ibrahim Cifci An examination of representations for USA in tourism brochures for Chinese market Yasong Wang An exploratory study on cognitive internship perception of tourism students Ozge Buyuk and Gulsah Akkus Are you afraid to travel during COVID-19? Gulsum Tabak, Sibel Canik, and Ebru Guneren Destination management during the health emergency: A bibliometric analysis Valentina Della Corte, Giovanna Del Gaudio,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.206 | 0.107 |
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".