City in Code: The Politics of Urban Modeling in the Age of Big Data
Bibliographic record
Abstract
A model is "any representation or concept that helps us to understand the world whenever common sense or direct observations are inadequate." Common sense and direct observation often prove inadequate to the complexities of the twenty-first-century cities. Thus, models abound in urban life and governance. However, a model is not only a tool for control but a way of defining a situation. Framing the city so as to render it susceptible to interpretation and intervention is an exercise not merely with scientific or technological value but with rhetorical power. The tradition of comprehensive urban models, beginning with the advent of computers and culminating in the self-analyzing "smart city," I argue, sidelines this rhetorical power in favor of a tone of scientific authority that, while justifiable in technical domains, does not legitimately scale to the level of a political community. Making good on the civic potential of Big Data thus requires recontextualizing properly scientific enterprises within an adequate political philosophy of the city, allowing for the construction of cultural urban models that set human freedom at the core of its inner workings.
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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| 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".