Bibliographic record
Abstract
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.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".