Bibliographic record
Abstract
In this article, I take the case of data centers as a powerful tool and infrastructure of multinational digital capitalism, analyzing the ways in which understanding these and other data infrastructures through their energy frameworks allows us to theorize the implications of planetary environmental impacts of digital data for contemporary subjects beyond individual data technologies themselves. This is especially true in data centers’ function as energy vacuums and in their carbon and extractive footprints and other environmental externalities. I demonstrate that data centers organize an assemblage of environmental relations whose operations reproduce uneven systems of capitalism enacted through energy and environmental politics. While this article is by no means comprehensive, and by necessity must be selective in its engagement with key texts in a number of overlapping fields, it broadly draws from media studies, geographical, and sociological approaches to data infrastructures to unravel the entanglements of digital systems and the environment. Data centers and their energy connections represent multivalent sites and indications into the global supply chain of data infrastructure, and their extractive dynamic as networked infrastructure fundamentally changes how we need to see their impacts and the impacts of datafication more broadly.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".