Bibliographic record
Abstract
Abstract | Why are the walls, floors, and ceilings of data centres always painted white? Photographs of data centre interiors tend to focus on the advanced technologies contained within them, while the surrounding white surfaces disappear into the background. Bringing this overlooked design feature to the foreground, this essay explores the technical functions, temporalities, and transparencies of white space within data centres.Résumé | Pourquoi les murs, les planchers et les plafonds des centres de données sont-ils toujours peints en blanc? Les photographies de l’intérieur des centres de données ont tendance à se concentrer sur les technologies de pointe qu’elles contiennent, tandis que les surfaces blanches environnantes disparaissent en arrière-plan. En mettant en lumière cet élément de conception souvent négligé, cette étude explore les fonctions techniques, les temporalités et les transparences de l’espace blanc dans les centres de données.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.006 | 0.026 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".