Artificial floors, subfloors, slabs. Comparative historical study of New York, Montreal and Boston
Bibliographic record
Abstract
Today, as in the past, in any growing metropolis - whose territory is not, a priori, infinitely expandable - the question arises of "vertical" urban development, exploiting the potential of each plot from the subsoil to the sky. The slab, like the tower, is one of the many ways of making the same land area profitable. In France, there are many examples of such negative perceptions in large housing estates and in Paris, where slabs dominate and isolate themselves from their urban context. This raises the question of the conditions for the success of artificial floors that function in osmosis with the surrounding city. Are there any examples of slab operations that are satisfactory in terms of their legibility, appearance, use and relationship to the rest of the city? What is the ownership status of these spaces?What were - or are - the technical and financial conditions for their implementation, and under what conditions was - or is - the profitability of land creation guaranteed?Who were - or are - the players involved in these operations (project owners and contractors, local authorities, private sector, etc.)? Finally, can these examples enable us to measure the system's capacity to renew itself through densification and substitution?Three American examples provide food for thought: 1° New York: the Grand Central Terminal slab; 2° Montreal: the business center and the underground city; 3° Boston: the project to bury the skyway. A cross-analysis of these three metropolises shows how the issue of vertical urban planning has been tackled in the past - and how it is being tackled today in North America - using the principles of towers and slabs to meet both the high demand for land and the growing need to serve city centers. Could we learn a few lessons from this in Paris?Translated with DeepL.com (free version)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.001 |
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".