Global technology companies and the politics of urban socio-technical imaginaries in the digital age: Processual proxies, Trojan horses and global beachheads
Bibliographic record
Abstract
In this paper, we take the concept of ‘new urban spaces’ as our jumping off point to engage with the efforts of Alphabet/Google affiliate Sidewalk Labs to cultivate a new integrated digital and infrastructural urban space on the Toronto waterfront. We interrogate the process and politics of imagining this new, digital urban space as an urban socio-technical imaginary. The paper critically examines the central role of ‘big tech’ in producing the urban socio-technical imaginary not as a snapshot but, rather, as a ‘process of becoming’. This processual focus on the role of big tech allows us to develop three interrelated analytical contributions. First, we generate in-depth understanding of the proxy politics of urban socio-technical imaginaries in constituting new digital urban spaces. Second, we argue that an urban socio-technical imaginary was used as a Trojan horse to promote private experimentation with urban governance. Third, we demonstrate attempts to imagine a global beachhead via ‘the global model’ of a new digital urban space predicated on the digital control of integrated urban infrastructure systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".