All That Is Urban Melts into Data: Circulations of Matter, Energy, and Data in the Digital City
Bibliographic record
Abstract
This article seeks to conceptualize the interrelated movement of data on the one hand, and matter and energy on the other hand—and therefore contributes to a theorization of data as simultaneously constitutive of, and constituted by, contemporary urbanism. To pursue this objective, we argue for close dialogue between urban political ecology (UPE) on the one hand, and science and technology studies (STS) and critical data studies (CDS) on the other hand. More specifically, we call for a closer examination of how data move, accumulate and agglomerate, and we propose that a conceptual distinction between “flows” and “circulations” can help operationalize this research agenda. We contend that this dialogue opens exciting research perspectives for both fields. From a UPE perspective, paying closer attention to data circulations is a way to advance poststructuralist approaches because it allows for fine-grained analysis of how resources (data, matter, and energy) are circulated, transformed, and accumulated to produce new urban natures. From an STS and CDS perspective, adopting a metabolic perspective on data circulations helps to reconceptualize the urban and analyze cities as sites of data agglomeration, accumulation, and contestation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".