Cloud ruins: Ericsson's Vaudreuil-Dorion data centre and infrastructural abandonment
Bibliographic record
Abstract
The past decade has seen the accelerated growth and expansion of large-scale data centre operations across the world to support emerging consumer and business data and computation needs. Built out rapidly, these emergent digital infrastructures carry the promise for new local industrial futures, all while their paths to obsolescence are shortened. Their lifespans are dependent on financial speculation, shifting corporate strategies, and advances in consumer technology. In this article we track the promise and afterlife of an abruptly abandoned data centre constructed by the global telecom giant Ericsson in Vaudreuil-Dorion, a town near Montréal, Québec, Canada, in order to expand emergent debates about digital ruination. Employing site visits, press reports, and qualitative interviews with architects and staff involved with the data centre's development in Sweden and Canada, we propose ‘cloud ruins’ as a sensitising concept to capture some of the specific meanings and material articulations that the abandonment of global data infrastructures may evoke in local contexts. Simultaneously familiar and novel, cloud ruins anticipate an emergent landscape of post-digital ruination that unfolds in the built environment in peripheral communities, part of the global logistical cities from within which our contemporary understandings of digitalisation are produced.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.027 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".