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Record W4388032196 · doi:10.1177/23996544231194828

Coercive geographies: Biopower, spatial politics, and the tourist

2023· article· en· W4388032196 on OpenAlexaff
Benjamin Lucca Iaquinto, Joseph M. Cheer, Maartje Roelofsen, Claudio Minca, Chin‐Ee Ong, Cora Un In Wong, Dominic Lapointe, Meng Qu, A K McCormick, Chih‐Chen Trista Lin

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBiopowerTourismSociologyPopulationPoliticsCoercion (linguistics)GovernmentalityPolitical scienceEnvironmental ethicsLaw

Abstract

fetched live from OpenAlex

This symposium examines the relations between biopower, destination governance and tourism. Biopower, a Foucauldian concept, refers to political strategies based on humanity’s biological features. In the simplest of terms, it is applied via biopolitical mandates that govern life of a given population. Contemporary tourism exemplifies the exertion of biopower over the mobility of travellers, as was evidenced during the COVID-19 pandemic, and to a lesser degree, continues to do so. The role that tourism plays in enabling authorities to enact spatial transformations reinforcing state power, while also indicating potential means of resistance, is foregrounded in this symposium. The four empirical contributions extend biopolitical thought by demonstrating that biopower is instrumental in the practices and regimes of mobility, security, in/exclusion of tourism. In Europe, the Dutch government experimented with enclosed “COVID-safe” tourist spaces. In Macao, China’s border regime screened tourists based on their viral threat capacities. On Naoshima Island in Japan, museums have transformed into infrastructures of bodily control. In Taiwan, flight attendants are grappling with newly emerging forms of biopower shaping the sociality of air travel and their own practices of hospitality. These empirically informed contributions interrogate how tourism figures in attempts to govern bodies at the population level, while uncovering the modes of coercion applied to govern tourists and the spaces they inhabit.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.016
GPT teacher head0.280
Teacher spread0.264 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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