The Ballet of the Streets: Teaching about Cities at Street Level
Bibliographic record
Abstract
The urban scholar Jane Jacobs once described city life as “the ballet of the streets.” In more than a quarter-century of joint teaching, we have used Jacobs’ metaphor to help our students understand that cities are living organisms created and maintained, for good or ill, by the people who live and work in them. At the heart, our teaching are intense encounters with cities, a “street-level” experience designed not only to give students a chance to walk the city’s streets (especially streets lying far off the beaten path), but to meet its people, prominent and not, so that they can discover for themselves, in living context, the city’s culture, varying life-styles, and issues. Once they learn that cities are people, our longer-term hope is that they will become active in the cities and urban regions which almost assuredly lie in their futures. Given their international importance and astronomical growth over the last half-century, it is arguable that cities are the most significant social systems in the world and, as a result, are crucial for students to understand as cities. The purpose of this paper is to share, first, the methodology we have developed for studying cities “at street level”; and second, to suggest how that methodology might be used in the study of cities anywhere. Starting with a course comparing New York and Toronto, we have used a similar approach to study cities in England, Ireland, Italy, Central Europe, China, and Vietnam.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".