Upscaling Downtown: Interpersonal Dynamics of Nightlife Revelers in Geneva, New York
Bibliographic record
Abstract
Bars have long been recognized as the intersection of a city’s culture and commerce. They provide opportunities for social interaction, contain a multitude of local memories, and serve as sources of identity. The American Revolution, the Whiskey Rebellion, and the Stonewall riots all developed out of local bars. So, what does it mean when the character of bars in a neighborhood begins to change? How do these changes to commercial spaces affect the social fabric of a city? Using a combination of ethnographic fieldwork and interviews, I explore the upscaling of the downtown bar scene in Geneva, New York to unpack what these commercial changes mean for the disparate groups that frequent the downtown space. I argue that instead of simply diversifying the types of businesses available to consumers in Geneva, this development has altered the very character and social fabric of downtown. Rather than creating an integrated and cohesive nightlife scene in which disparate groups come together in shared space and time, this development manifests in the fragmentation of the downtown scene in new ways that increase the segregation of people in social space.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".