Introduction – Controlling mobile guests where they rest. Comparative perspectives on hotel registration in Europe, 1840s–1930s
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".