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Record W4387131379 · doi:10.5539/ibr.v16n10p24

Tourism Potential of China and Its Relevance for Jordan’s Economic Openness

2023· article· en· W4387131379 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Business Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsTourismChinaRevenueBusinessTourism geographyEcotourismMarketingQuality (philosophy)Economic growthEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

The article dwells on the analysis of the international tourism industry in China and directions of implementation of its positive innovations in the tourism industry in Jordan. The paper analysis data, characterizes the current state and examines the strategic directions of the development of the tourism industry in China. The tourism sector in Jordan is considered as one of the most promising. The aim is to turn tourism in Jordan from a seasonal into a year-round activity. Jordan is a small economy compared to China. Nevertheless, common features of economic recovery after pandemic and great counts on tourism as a source of continuous revenues make positive Chinese experience relevant for Jordanian economy. Additionally, Jordan has become an up-and-coming destination for Chinese tourists. We expect that improvements in Jordanian tourism sector will serve the same “accelerator button” as it worked for China. The practical significance of the study is to determine the directions for improving the international tourism industry in Jordan, namely the development of ecotourism and tourism for the elderly (medical tourism, historical and cultural tourism, social tourism, relaxation tourism); introduction of innovations and digital technologies (digital platforms and online booking, virtual tours and augmented reality, face recognition and other security technologies, artificial intelligence and data analytics, interactive multimedia technologies, smart tourism and the Internet of Things); development of business tourism; improvement of the quality of tourist services (staff training, strengthening control monitoring the quality of services and updating the infrastructure of tourist facilities); development of new forms of tourism (individual and thematic tours, virtual reality tours and tours related to cultural and educational experience); development of individual programs for tourists; improvement of public services and management systems in the field of tourism.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.051
GPT teacher head0.364
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it