The Biosecuritization of the Tourist City
Bibliographic record
Abstract
The impact of COVID-19 on tourism has been enormous across the globe. The successful recovery of the tourism industry at the local, national, and global levels is strictly dependent on the efficient contention and mitigation of the COVID-19 pandemic at the global level and on the capacity of tour operators, governments, and other actors to generate complete trust among tourists. In this article, we examine the biosecuritization of Lisbon (Portugal) and the efforts carried out by the administration to preserve the city as a COVID-free urban destination. In this sense, we will examine two main strategies that have received little attention from the scholarly community, namely (i) the strengthening of repressive, punitive, and criminalizing policies against suburban working-class youths ('the perilous') within the scope of guaranteeing a COVID-free city for tourists ('the untouchables'), and (ii) the (in)governance of the urban night of Lisbon during the current pandemic. In the last section, we will argue how mobility restrictions, lockdowns, and nighttime curfews have shown us how central culture, arts, entertainment, and leisure are for not only the cultural and social life of many young and adult people in Europe but also for their socio-emotional wellbeing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".