DISSOLVED CLOUDS: ERICSSON'S VAUDREUIL DATA CENTRE AND INFRASTRUCTURALABANDONMENT
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. These buildings, as infrastructures responsive to changing global economic and technological terrain, are increasingly modular, and must be built out rapidly. However, these conditions also mean that their paths to obsolescence are shortened, their lifespans dependent on shifting corporate strategies and advances in consumer technology. This paper theorises and empirically explores material, infrastructural abandonment that emerges in this process of data centre construction across different geographical contexts. To do so, we analyse the socio-material construction of an international network of large-scale data centres by global telecom giant Ericsson, and the abrupt abandonment and suspension of one of its nodes in Vaudreuil, Québec in 2017 after only nine months of operation. Employing autoethnography, site visits, and qualitative interviews with data centre architects and staff in Sweden and Canada, we argue that the ruins of abandoned 'cloud' infrastructure represent the disjunction between the 'promise' of digital infrastructure for local communities and the market interests of digital companies. With its focus, the paper takes ruination and discard as perspectives through which to understand the complexity of emergent datafied futures and the socio-technical reshaping of internet infrastructures.
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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 | 0.003 | 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 | 0.020 | 0.020 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".