Bibliographic record
Abstract
Abstract | Originally housed in generic industrial buildings, data centres have become sites of architectural feats and playgrounds for starchitects in recent years. These buildings testify to a changed role of how we think of these repositories for data and their position in our society. Through a reading of the Bahnhof data centre Pionen in Stockholm from 2008 and the design schematic for a Data Tower in Iceland, this article examines how the data centre as an architectural and infrastructural edifice facilitates data storage and access, focusing on how security is articulated in the architectural vocabulary through negotiations of visibility. By intermingling images of these sites with textual vignette-like reflections, this article uses the architecture of the data centre to address how the design of dynamic data archives embodies cultural imaginaries of uncertainty through the tropes of shelter and exposure.Résumé | Initialement hébergés dans des bâtiments industriels génériques, les centres de données sont devenus récemment des merveilles d’architecture et des terrains de jeux pour les « starchitects ». Ces bâtiments témoignent d’un changement de rôle dans la façon dont nous pensons à ces entrepôts de données et à leur position dans notre société. Grâce à une lecture du centre de données Bahnhof Pionen à Stockholm à partir de 2008 et au schéma de conception d’une tour de données en Islande, cet article examine comment le centre de données, en tant qu’édifice architectural et infrastructure, facilite le stockage et l’accès aux données, en mettant l’accent sur la façon dont la sécurité est articulée dans le vocabulaire architectural à travers les négociations de visibilité. En entremêlant les images de ces sites avec des réflexions textuelles semblables à des vignettes, cet article utilise l’architecture du centre de données pour aborder comment l’élaboration de centre de données dynamiques incarne des imaginaires culturels d’incertitude à travers les tropes d’abris et d’exposition.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".