Seeking New York the stories behind the historic architecture of Manhattan - one building at a time
Bibliographic record
Abstract
Based on the popular blog Daytonian in Manhattan, Seeking New York investigates the back stories of Manhattan's architecture and monuments. Alongside the expected account of architects, dates and styles, it reveals the human history of the buildings and statues: the scandals, the tribulations, the joys and achievements, the humanity, indeed, of the New Yorkers who lived within these walls. Meet Dorothy Parker, S.J.Perelman, Talullah Bankhead and Irving Berlin at the Algonquin Round Table; Maisie Plant, who persuaded her husband to sell his Fifth Avenue palazzo to Cartier for $100 and a pearl necklace; James and Abby Gibbons, whose Chelsea home was one of the stations on the Underground Railroad by which fugitive slaves found their way from the South to Canada. Perhaps you would rather not meet Jack the Rat, who for a dime would bite the head off a live mouse (for a quarter he'd do the same to a rat); or Ivan Poderjay, who left his bride's apartment for their honeymoon - with her body in a steamer trunk. Here the ever-changing face of Manhattan is captured through the structures and their stories
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.005 |
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".