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Record W4402574369 · doi:10.1080/14649365.2024.2403098

# <i>SpendYourSummerInGeorgia</i> : popular geopolitics, grassroots activism and tourism marketing against Russia

2024· article· en· W4402574369 on OpenAlexaff
Suzanne Harris-Brandts, David Gogishvili, David Sichinava

Bibliographic record

VenueSocial & Cultural Geography · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsCarleton University
FundersShota Rustaveli National Science Foundation
KeywordsGrassrootsGeopoliticsPolitical scienceTourismSociologyPolitical economyPoliticsLaw

Abstract

fetched live from OpenAlex

This article demonstrates the broad-spanning ramifications of tourism marketing in geopolitics and proposes viewing civilian-led social media destination branding campaigns as novel yet important forms of popular geopolitics. The case is the #SpendYourSummerInGeorgia campaign created by Georgian grassroots activists in 2019 following a politicized travel blockade issued by the Kremlin preventing Russians from entering Georgia. #SpendYourSummerInGeorgia was designed to counter the blockade, soliciting an alternative, pro-Western and European tourism audience. It enabled citizens to engage in their country’s foreign relations – a space historically reserved for political elites – yet one now accessed through tourism marketing. This campaign also shaped representations of Georgian collective identity, including those linked to Europe and the Soviet Union, thus ordering social, cultural and political values in the country. Contributing to literature across popular geopolitics, tourism geographies and nation branding, this article uses content analysis and semi-structured interviews to show how tourism was not only impacted by geopolitics but also became its very medium. As popular tourism marketing enters the messy world of geopolitics, this case demonstrates how the stakes for cultivating a strategically favourable collective identity are high, calling for those studying popular geopolitics to have their radar attuned to 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
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 head0.020
GPT teacher head0.313
Teacher spread0.293 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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