Bibliographic record
Abstract
Abstract Urbanists have developed an extensive set of propositions about why gay neighborhoods form, how they change, shifts in their significance, and their spatial expressions. Existing research in this emerging field of “gayborhood studies” emphasizes macro-structural explanatory variables, including the economy (e.g., land values, urban governance, growth machine politics, affordability, and gentrification), culture (e.g., public opinions, societal acceptance, and assimilation), and technology (e.g., geo-coded mobile apps, online dating services). In this chapter, I use the residential logics of queer people—why they in their own words say that they live in a gay district—to show how gayborhoods acquire their significance on the streets. By shifting the analytic gaze from abstract concepts to interactions and embodied perceptions on the ground—a “street empirics” as I call it—I challenge the claim that gayborhoods as an urban form are outmoded or obsolete. More generally, my findings caution against adopting an exclusively supra-individual approach in urban studies. The reasons that residents provide for why their neighborhoods appeal to them showcase the analytic power of the streets for understanding what places mean and why they matter.
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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 | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".