Bibliographic record
Abstract
In this article, the authors demonstrate how the data center has become a key site, object, and metaphor for interdisciplinary scholarship of the internet. While the data center is a fabrication of engineering, computer science, and cognate fields, it has been the critical gaze of scholars outside of those industries. Together, this scholarship has established the field of Critical Data Center Studies. Critiques of the data center – often thought of more generally as ‘internet infrastructure’, and more evocatively as ‘the cloud’ – have emerged from the social sciences, humanities, journalism, and the arts. The authors do this by answering questions about the current social, cultural, political, and environmental landscapes of the data center. Scrutiny of the foundational imaginaries of the internet, real estate deals by Big Tech, the industry’s enabling policies, their connections to energy and other public infrastructure – among many other factors – serves, at the very least, to situate the data center as a media object, as more than simply a material infrastructure, as more than data warehouse, and as more than ‘the cloud’. Further to this, the authors reflect on how the data center has been and continues to be studied, and why critical interventions have been so fruitful within a vast array of disciplines – from history and anthropology, to media studies, information studies, and science & technology studies – for shifting the focus from questions of infrastructural visibility to questions that weave together concerns of efficiency, policy, popular culture, and planetary devastation.
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 | 0.193 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.027 | 0.142 |
| Scholarly communication | 0.047 | 0.076 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.017 | 0.033 |
| 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".