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Record W2910804566 · doi:10.1111/amet.12735

Data centers as infrastructural in‐betweens:

2019· article· en· W2910804566 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

VenueAmerican Ethnologist · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsQueen's University
FundersAmerican-Scandinavian FoundationWenner-Gren FoundationAmerican Council of Learned SocietiesNational Science Foundation
KeywordsMilitarismCloud computingTransformative learningLandfallSpace (punctuation)PeninsulaHistorySociologyArchaeologyPolitical scienceGeographyComputer scienceLawPoliticsMeteorology

Abstract

fetched live from OpenAlex

ABSTRACT On Iceland's Reykjanes peninsula, a new industry is taking root in the ruins of a US military base: digital data storage. The new data centers, where transnational corporations pay to store terabytes of information, have been lauded as transformative for the region. But as they engage the military base's physical infrastructures, spatial orders, and affective resonances, they reprise and cement Reykjanes's former role as an infrastructural in‐between : a node in others’ networks, both built in and left out. Thus, while digital networks are often imagined as overcoming marginality through the “death of distance” or “compression of space‐time,” their layering amid imperial legacies means that on Reykjanes they perpetuate marginality. These conditions illustrate the unevenly emplaced impacts of cloud computing and unsettle the techno‐utopian ideal of connectivity. [ infrastructure, information technology, data centers, militarism, intermediarity, marginality, Iceland ]

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.033
GPT teacher head0.299
Teacher spread0.266 · 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