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Record W4400415371 · doi:10.1080/1369183x.2024.2375364

Schengen visa marketing in China: the street-level competition to attract tourists to Europe

2024· article· en· W4400415371 on OpenAlexfundno aff
J Dupont

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
FundersFonds de Recherche du Québec-Société et CultureConcordia University
KeywordsChinaCompetition (biology)AdvertisingBusinessMarketingEconomic geographyPolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

Before COVID-19, visitors from China, were a prime target for the European tourism industries. Yet, their mobility was constrained by the Schengen visa requirement for any trip to a European Union (EU)’s Member States. While the literature on Schengen visa policy has highlighted the repressive practices of street-level bureaucrats processing visa applications abroad, this article seeks to understand how Schengen visa policy is implemented when the objective is to attract potential visitors rather than drive them away. The paper argues that the economic imperative to attract Chinese tourists to Europe is turning local consular cooperation from Schengen into local consular competition. To support this claim and using ethnographic methods as well as a relational approach to implementation, the paper develops the concept of visa marketing to analyse the race to attractiveness between French and Italian consulates based in Beijing, the capital of China. Visa marketing refers to the use of visa procedures (receipt conditions, reliability, processing speed, etc.) by consulates as sales arguments to advertise the destination they represent. In essence, the article presents a case of domestic actors appropriating common visa regimes, engaging in competition to entice foreign consumers to their territories.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.981

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 science0.0000.000
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 head0.103
GPT teacher head0.441
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2024
Admission routes1
Has abstractyes

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