Of Flying Cars and Pandemic Urbanism: Splintering Urban Society in the Age of Covid-19
Bibliographic record
Abstract
Disappointingly to many who grew up at the time, promises of flying cars in the 1960s as a future form of urban transportation were not kept. That future never arrived. In this short commentary, I want to board the metaphorical flying car and steer it into a different direction. At the height of the first wave of Covid-19, a more widespread sentiment took hold that saw the anticipation of increased mobilities dashed by a general anticipation of disaster considered typical for our age today. We might conclude: We don't get the technologies we want because we have left the era of technological progress and entered the era of risk and anticipation of disaster. My commentary appreciates and discusses the lessons we can learn from Splintering Urbanism for our period of pandemic urbanism. How does the kind of networked urbanism that the book examines and critiques provide a framework in which we can understand the emergence, presence, and management of the pandemic as it affects our urban world today?
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".