A RIVER OF DATA RUNS THROUGH IT: EXAMINING URBAN CIRCULATIONS IN THE DIGITAL AGE
Bibliographic record
Abstract
There is a deepening need for dialogue between (digital) urbanists and Internet Studies scholarship. In this paper we are interested in "urbanizing" Internet Studies by thinking about how digital infrastructures create and control circulations, movement, flows, and streams within urban contexts. More specifically, we think about circulations and concentrations of natural, human, and digital resources by way of Urban Political Ecology to better understand smart cities, digital urban labor, and Anthropocene literatures. As data, infrastructures, apps, capital, and natural phenomena concentrate in cities, and are instantiated to create and constrain flows and circulations, we contend that Internet Studies can play a key role in analyzing and understanding these new socio-technical entanglements. Drawing on Nost and Goldstein's notion of "data infrastructures", we think about how the materiality of data and digital technologies shape cities, and cities shape data and technologies. We suggest several conceptual and methodological overlaps with Urban Political Ecology, to signal what an urbanized Internet Studies, concentrated on circulations and flows, might look like.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".