The rise of urban tech: how innovations for cities come from cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research investigates the economic geography of urban technology, or ‘urban tech’, start-up enterprises. Comprised of ride-hailing, co-living, co-working, smart cities and other urban-oriented activities, urban tech is a suite of innovations that enable and are premised upon growing urbanization. We investigate where urban tech comes from by analysing Pitchbook, a database of venture capital deals, to chart the evolution and geography of urban tech start-up firms. We show urban tech firms to be highly clustered in two kinds of places: specialized tech hubs such as the San Francisco Bay Area and large cities such as New York, London and Beijing. Furthermore, we find that urban tech geography is associated with two classes of factors: the scale of existing tech activity, and the size and extent of metro areas. Together these findings suggest that the geography of urban tech is shaped by the innovative capabilities of urban areas and, to a lesser extent, by urbanization itself. Urban tech investment is less common in areas associated with ‘Industry 4.0’ industrial policy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it