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Record W3014467437 · doi:10.1177/2399654420915573

Staging Israel/Palestine: The geopolitical imaginaries of international tourism

2020· article· en· W3014467437 on OpenAlexaff
Connie Guang-Hwa Yang

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeopoliticsThe ImaginaryTourismState (computer science)FrontierDistancingPalestinePolitical scienceDark tourismSociologyGeographyPolitical economyMedia studiesHistoryLawAncient historyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

This article argues that the curation of particular geopolitical imaginaries of Israel/Palestine for international tourists can legitimize and naturalize the violence of the Israeli state project. Juxtaposing the cases of Tel Aviv-Jaffa and the West Bank, I analyze the discourses and embodied practices that produce imaginative geographies through processes of spatial distancing and temporal fixing. The dominant imaginary in Tel Aviv-Jaffa incorporates Israel into a westernized geography of Europe, while the dominant imaginary of the West Bank emphasizes its location in an Orientalized Middle East. The cultivation of these tourist landscapes as entirely disparate places works to obscure how both are constitutive of a single Israeli regime, contributing to the public secret that separates the occupation of the West Bank from Israel as a democratic state. By examining how seemingly apolitical tourist practices are entangled with geopolitical violence, this article reveals the complicity of international tourism in sustaining Israeli settler colonial dispossession and military occupation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.034
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2020
Admission routes1
Has abstractyes

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