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Record W3130489973 · doi:10.17613/cw2d-jz18

Managing Carbon and Data Flows: Fungible Forms of Mediation in the Cloud

2019· article· en· W3130489973 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHumanities Commons CORE (Modern Language Association / Columbia University) · 2019
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsTrent University
Fundersnot available
KeywordsMediationCloud computingComputer sciencePolitical scienceOperating systemLaw

Abstract

fetched live from OpenAlex

Microsoft's transition to being both a cloud company and a carbon neutral company occurred at the same time and with common structuring logics. Cloud computing, carbon offsetting, and renewable energy provisioning share place-agnostic structures and practices. This article examines these trends together, analyzing how global forms of mediation and management mutually reinforce one another across the cloud's carbon and data flows. This analytic affords more than just a tale of a commodity and its (mis)managed environmental externalities, but rather an analysis of the structures of fungible mediation common to both alike. Understanding this concept and its resistance to place-based accountability not only better captures the empirical and aspirational actions of cloud computing, it also allows for more efficacious forms of green media critique.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.211
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it