The Factories of the Past Are Turning Into the Data Centres of the Future
Bibliographic record
Abstract
Abstract | This essay traces the history and geography of data’s materiality by examining the transformation of industrial building stock in Chicago to serve the needs of the data industry. Using contemporary and archival photographs as entry points, the paper unpacks the rise of an information-based economy in relation to the decline of an industrial economy. Buildings where workers once processed checks, baked bread, and printed Sears catalogues now route packets of information and host servers engaged in financial trading. Thus, contained within the physical transformation of some of Chicago’s buildings is a larger historical and geographical narrative about the uneven development of capitalism. This historical view reminds us that infrastructure is, and always has been, political. Résumé | Cet essai retrace l’histoire et la géographie de la matérialité des données en examinant la transformation des bâtiments industriels à Chicago pour répondre aux besoins de l’industrie des données. En utilisant des photographies contemporaines et d’archives comme point de départ, le texte explore la montée d’une économie axée sur l’information par rapport au déclin d’une économie industrielle. Les bâtiments où les travailleurs ont autrefois traité des chèques, cuit du pain, et imprimé les catalogues Sears, transmettent maintenant des paquets d’information et hébergent des serveurs impliqués dans les échanges financiers. Ainsi, à travers la transformation physique de certains bâtiments de Chicago, il existe un vaste récit historique et géographique à propos du développement inégal du capitalisme. Ce point de vue historique nous rappelle que l’infrastructure est, et a toujours été politique.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".