Scoreboard Urbanism: Theorizing Mental Life in the Digitally Mediated Metropolis
Bibliographic record
Abstract
Georg Simmel famously argued that the sensory onslaught of the urban environment forces people to reduce the world to calculable quantities over colorful qualities and adopt a blasé attitude of muted emotions. Today’s digitally mediated city involves levels of quantification that Simmel could have scarcely imagined. However, rather than exacerbating the blasé attitude, this paper makes the case that digital technologies potentially increase our emotional and moral attachments to the urban environment—a phenomenon that can be called “scoreboard urbanism.” From Yelp ratings to Fitbit step scores, our relationship to the city is increasingly mediated by quantitative metrics. The purpose of this paper is to outline the basic characteristics of scoreboard urbanism as a distinct mode of life that entails new ways of perceiving and interacting with the urban public realm. In doing so, the paper argues that this phenomenon has transformed the city into a “gamespace” characterized by the competitive and exhilarating drive to score points.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".