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Record W4417129992 · doi:10.1080/02619288.2025.2591362

Introduction – Controlling mobile guests where they rest. Comparative perspectives on hotel registration in Europe, 1840s–1930s

2025· article· en· W4417129992 on OpenAlexaff
Jasper Segerink, Torsten Feys, Hilde Greefs, Kevin J. James

Bibliographic record

VenueImmigrants & Minorities · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMobile deviceKey (lock)Tourism

Abstract

fetched live from OpenAlex

At the intersection of tourism and migration history, this special issue focuses on the roles of registration documents and practices in European hotels during the nineteenth and early twentieth centuries. Despite playing a key role in surveillance, the role of hotel registration has been less studied in a context of increasing migration and state control. Here, hotel registration is comparatively studied from three perspectives. Firstly, attention is given to politics, or the changing role of state authorities in relation to mobility and migration, and in particular to the selective implementation of bureaucratic practices. Secondly, attention shifts to people and how they were registered by the various non-governmental and governmental actors involved at different administrative levels. Thirdly, the articles deal with performances and the tensions between theory and practice: how registration practices varied between localities and how travellers themselves could use these practices as a tool for self-identification or protection. Through case studies covering Europe from Austria to Switzerland, Belgium and the United Kingdom, we reveal how hotel registration formed part of broader systems of population and migration control and demonstrate that the boundaries between ’migrants‘ and ’tourists’ were fluid and varied over time and by region.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.941

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 studies0.0010.000
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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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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