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Record W4407970450 · doi:10.1079/tourism.2025.0007

The Slow and Resolute Resurrection of the Destination Sarajevo

2025· article· en· W4407970450 on OpenAlexaff
Cyril Martin-Colonna

Bibliographic record

VenueTourism Cases · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

Summary Between 1992 and 1995, Bosnia and Herzegovina suffered a major armed conflict. This brought major disruption of the urban system to the capital, Sarajevo. This conflict caused a fracture inside the society and its urban identity. After the immediate reconstruction stage, a new process called post-conflict transition, event or disaster followed and required the collaboration of actors at multiple scales. This promoted the realization of new urban and societal projects. The tourism sector is one of these projects, creators, and carriers of new representations of the city, promoting the reconstruction of the tourist destination and the renewal of urban identity through a new tourism development of the city, which promotes the memory of the past conflict. These representations, modelled by the different urban actors (local and national, international, tourist and cultural policies), are transmitted to the tourist level through the urban space, bringing them to the attention of tourists, which can promote territorial attractiveness and economic benefits for local populations. However, following a conflict, the decision making actors of the city can make conflicting choices, particularly in the case of cities that have experienced internal conflicts. The case highlights the situation of Sarajevo between 2022 and 2024, increasingly embracing the European model of the cultural and sustainable tourist city, while preserving the memory of the past conflict, against a backdrop of political and identity crises. Information © The Author 2025

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.019
GPT teacher head0.304
Teacher spread0.285 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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