The Making of Urban Knowledge: Ideas, Cities, Gurus
Bibliographic record
Abstract
In the face of severe political, economic and environmental crises at the urban level, cities have been seen in recent decades not only as the place where these problems are created, but also as the location where they could be solved. This construction of cities as sites of both crisis and opportunity is related, at least partly, to the work of urban experts, transnational organizations, professional networks, and local agencies taking part in developing and connecting cities with ‘big ideas’ like Resilient Cities, Smart Cities and Creative Cities. To examine the making of such ideas, this dissertation develops the theoretical model of the Urban Knowledge Making Triangle, joining together three different units of analysis - Ideas, Cities, and Agents. The triangle calls to investigate how the ‘same’ idea varies over time and across places, how cities use the idea to depoliticize and re-politicize local struggles, and how human agents connect cities with the idea. To demonstrate the approach, I focus on the Creative City paradigm, which describes the shift that cities took when adjusting their economic industrial base to the information age. It also offers a formula to recover from the urban decline that many cities experienced during post-industrialization. The Creative City long-life cycle and flexible formula which is applicable to cities of different sizes and geography, make its local translations vary extensively. I use fieldwork methods to examine the making of the Creative City in Jerusalem and Toronto and the role of Richard Florida in connecting the two cities with the idea; I also use Structural Topic Modelling to draw the broader contours of arts and culture discourses using a policy corpus of the largest 26 cities in the Anglo-Saxon world. The findings demonstrate the dialectic relationship between the construction of cities as sites of crises and as sites of opportunities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.016 | 0.098 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".