Bibliographic record
Abstract
“Fantasy City” refers to a new urban form located at the intersection of leisure, consumption, tourism, and real estate development. In Fantasy City: Pleasure and Profit in the Postmodern Metropolis (1998), Canadian sociologist John Hannigan points to six defining features: fantasy cities are characteristically themo‐centric (scripted), aggressively branded, active day and night, modular (mixing a standard array of retail and entertainment components), solipsistic (isolated physically and economically from the neighborhoods that surround them), and postmodern (in their reliance on simulation and spectacle). This set of phenomena is empirically manifested in an infrastructure of themed restaurants, nightclubs, shopping malls, multiplex cinemas, virtual reality arcades, casino‐hotels, book and record megastores, sports stadiums and arenas, and other urban entertainment destinations. While the only urban centers that currently qualify as full‐scale fantasy cities are Las Vegas and Orlando, Florida, most cities today have some commercial neighborhoods and developments that display these characteristics to a greater or lesser extent. Furthermore, fantasy city development has spread aggressively beyond the borders of North America, with large‐scale projects currently operating or under construction in such countries as Australia, Singapore, Malaysia, China, Saudi Arabia, and Dubai.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.138 | 0.035 |
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".