Bibliographic record
Abstract
In the spring of 2018, a small group of homeless people took shelter underneath the scaffolding at 1441 Broadway in Times Square after a massive rainstorm made its way through New York City. They hung their wet clothes on the steel bracing and slept on unfolded cardboard boxes. This makeshift encampment lasted for a few days before the police officially kicked everyone out.1 Two years before that, homeless New Yorkers set up a similar camp underneath the scaffolding in NoHo. Residents of NoHo blamed a surge of local construction for the implementation of sheds under which people could hide out.2 Yet another encampment existed in the Financial District in 2016, underneath a shed that was eight years old at the time.3 These are only some of the many examples of New Yorkers using scaffolding as a form of shelter. In New York City, “sidewalk shed” is the vernacular term for a type of scaffolding that covers a sidewalk immediately adjacent to a site in order to protect pedestrians from falling debris during the construction or renovation of that site. The design portion of my thesis contends that the New York City sidewalk shed should engage with and address the needs of the public, particularly the City’s under served population, by becoming an inhabitable architectural feature while still fulfilling its original purpose of protecting pedestrians from active construction sites. This multiple-site series of interventions follows a standard system but has varying “plug-ins” or alterations based on contextual elements such as architectural style and social conditions.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.210 | 0.027 |
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".