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Record W3169511155 · doi:10.31410/tmt.2020.555

ONLINE PLATFORMS AS MODERN TOOLS IN TOURISM IN EXTRAORDINARY TIMES: CASE FOR FURTHER DIGITALIZATION IN WESTERN BALKANS

2020· book-chapter· en· W3169511155 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Thematic Monograph. Modern Management Tools and Economy of Tourism Sector in Present Era · 2020
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionTourismConvergence (economics)The InternetAccommodationQuarter (Canadian coin)Argument (complex analysis)EconomyBusiness modelDigital economyWestern europeSharing economyBusinessPolitical scienceGeographyEconomicsMarketingInternational tradeEconomic growthComputer science

Abstract

fetched live from OpenAlex

Extraordinary times require an extraordinary response, especially when economic growth is at stake. Tourism contributes to economic growth in Western Balkans and has been robust in recent years. That was supported by new business models which make it possible for households to participate in the digital economy, including online platforms for travel accommodation. Internet connectivity and digital skills are crucial in that respect. This paper looks at the convergence of the Western Balkan candidate countries to the European Union (EU) in terms of connectivity and the digital skills needed. Analysis of data derived from questionnaires and other sources at the Eurostat show that candidate countries participate in the collaborative economy less and are below EU average in terms of digitalization. Investments supported by EU commitment to the region could improve internet connectivity and digital skills in the Western Balkans. That would benefit their economies which is especially relevant now considering the Covid-19 outbreak in the first quarter of 2020 and its disruption to achieving many goals. The argument for further digitalization is even more important.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.251
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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