Cities and visitors : regulating people, markets, and city space
Bibliographic record
Abstract
List of Illustrations vii List of Tables ix List of Contributors xi Series Editorsa Preface xv Preface xvi Introduction 1 Susan S. Fainstein, Lily M. Hoffman, and Dennis R. Judd Part I: Regulating Visitors 21 1 Visitors and the Spatial Ecology of the City Dennis R. Judd 23 2 Cities, Security, and Visitors: Managing Mega--Events in France Sophie Body--Gendrot 39 3 Sociological Theories of Tourism and Regulation Theory Nicolo Costa and Guido Martinotti 53 Part II: Regulating City Space 73 4 Amsterdam: It's All in the Mix Pieter Terhorst, Jacques van de Ven, and Leon Deben 75 5 Revalorizing the Inner City: Tourism and Regulation in Harlem Lily M. Hoffman 91 6 Barcelona: Governing Coalitions, Visitors, and the Changing City Center Marisol Garcia and Nuria Claver 113 7 The Evolution of Australian Tourism Urbanization Patrick Mullins 126 Part III: Regulating Labor Markets 143 8 Regulating Hospitality: Tourism Workers in New York and Los Angeles David L. Gladstone and Susan S. Fainstein 145 9 Shaping the Tourism Labor Market in Montreal Marc V. Levine 167 Part IV: Regulating the Tourism Industry 185 10 Mexico: Tensions in the Fordist Model of Tourism Development Daniel Hiernaux--Nicolas 187 11 The New Berlin: Marketing the City of Dreams Hartmut Haussermann and Claire Colomb 200 12 Museums as Flagships of Urban Development Chris Hamnett and Noam Shoval 219 Part V: Conclusion 237 13 Making Theoretical Sense of Tourism Susan S. Fainstein, Lily M. Hoffman, and Dennis R. Judd 239 Index 254
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.003 |
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".