Programming Place : The Question of the Smart City
Bibliographic record
Abstract
The Internet of Things and the lure of smart cities are poised to revolutionize the urban areas of North America. Digital technology has introduced ubiquitous communication and influenced the organization of urban areas in North America, and together this has changed the ways in which people use urban public spaces. Now, the possibilities of the integration of digital technology into the physical infrastructure of the city has technology companies eager to partner with municipalities to realize the economic and managerial benefits of big data. The realities of the implementation of the smart city concept, however, has raised myriad concern around the role of private interests in public life with regards to privacy, ownership, control, and inequality. Many of these concerns play out in public spaces, as they are integral to the enactment of public life in cities while also increasingly funded, and therefore influenced, by private interests. What, then, are future programmatic and technological possibilities for urban public space that seize the opportunities while addressing the concerns? This project proposal first seeks to understand the historical and contemporary roles and functions of urban public space and real estate development, as well as big data. Then, it explores the influence of technology on the human understanding and organization of space to unpack the influence of the Internet of Things. Finally, a design project is proposed for the public space in Sidewalk Toronto’s Quayside development that seeks to address these phenomena through the thinking of the philosopher Hannah Arendt.
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 | 0.002 | 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 | 0.010 | 0.044 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".