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Record W4324144823 · doi:10.21900/j.median.v19i1.936

Archiving for Extinction

2023· article· en· W4324144823 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

VenueMedia-N · 2023
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPresumptionCloud computingPoliticsBig dataSociologyPolitical scienceEnvironmental ethicsLawComputer science

Abstract

fetched live from OpenAlex

Anjali Arondekar, Wendy H. K. Chun, Verne Harris, N. Katherine Hayles, Shannon Mattern, Saidiya Hartman, and Kate Eichhorn, among other scholars of the archives, have questioned the presumption of the archive as complete, whole, legitimate, authoritative, and ultimately in any way “total,” by looking beyond the contents that the physical repository hosts and guards, as well as how, what, and who goes under-, mis- and altogether unrepresented. In their tradition, we find that the contemporary moment provides exemplars of where an archival (re)making is being uncritically taken up, increasingly envisioned, and subsequently reliant upon present-day technological capacities and the technological imaginaries of the future near and far. Under the guise of scientifically vetted global betterment, and drawing on a long legacy of publicly funded innovation that is then recaptured and taken up by private industry, Big Tech takes profit and credit for these particular future-oriented deployments, but takes on little to none of the social, political, and environmental responsibility. In this article we explore specifically what users can do when their abstracted data production or consumption is based not only on deeply flawed science and technology that is pervasive, powerful, and compelling, but also invariably presented as the only solution to climate catastrophe and the end of human existence. The three archival projects explored in this article—ordered by scale—are Alphabet’s “The Selfish Ledger,” Big Tech’s “Genomics in the Cloud,” and Arch Mission’s launch of a “Solar, Earth, Lunar, and Mars Library.” By exploring the sociotechnological imaginaries of Big Tech, we reposition the archive in terms of its legitimation and framing of humanity’s past, present, and future. We demonstrate that the ledger is a political frame, cloud-based genomics is a biological and terrestrial fix, and the space library is a speculative implementation of the total—and final—archive for extinction.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.007
Open science0.0010.000
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 head0.166
GPT teacher head0.386
Teacher spread0.220 · 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