Digital urban development - How large digital corporations shape the field of urban governance (DIGI-GOV)
Bibliographic record
Abstract
DIGI-GOV is a research project that aims to understand (I) the role of large digital corporations (LDCs) in digital urbandevelopment, (II) how the presence of LDCs in urban planning practice challenge pre-existing modes urban governance, and (III) how LDC-led urban development constitutes a new relational geography of digital cities. DIGI-GOV is thus a chance to call attention to this critical shift in the ways that contemporary digital cities are constructed, planned, mediated and governed. DIGI-GOV expands on prior research that examined Alphabet Inc.’s digital city project in Toronto that raised a number of important issues forurban planners, development practitioners, and urban studies scholars – even if this particular digital city project was ultimately unsuccessful. DIGI-GOV expands this research because the range of services that LDCs provide has increased in both volume and centrality; more and more public and private institutions rely on LDCs for essential digital infrastructures. There is an urgent need to study the trajectories of urbanization that are rolled out under the leadership of LDCs and the tensions in urban governance that are unleashed. DIGI-GOV will shed light on four further cities in addition to Toronto, which have been challenged by the presence of LDCs—namely, Seattle, Washington D.C, Bissen, and Eemshaven. The selected cities are some of the few exemplary cases available where LDCs have secured their position in the local urban field. Through qualitative methodological approaches, DIGI-GOV will tease out how these cities are relationally connected through LDC-led urban development, and what scholars and practitioners can learn from these experiences. Examined together, one can scratch at the surface of, and unearth, this new emerging relational geography.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".