Embedded dangers: the history of the year 2000 problem and the politics of technological repair
Bibliographic record
Abstract
This paper draws on archival research to revisit the Year 2000 Problem as an episode of recent technological history. I argue that the ways Y2K was addressed set the groundwork for the large-scale infrastructural management of technological contingency in the early 21st century. I approach the organized response to the perceived threat of the Y2K bug as one of the greatest, public-facing attempts to educate and train individuals and organizations to manage the unforeseen, and potentially devastating, effects old computer code can have on contemporary computerized infrastructures. This paper examines three key effects of the crisis: 1) the massive resource investment and funding expenditures on computerized infrastructures that few other crises have compelled; 2) the changes in insurance and tort law developed as a dimension of the crisis’ legal repair; and 3) the proliferation of risk management training around computerized infrastructures. By studying these three effects, this paper reconfigures the role of Y2K in the history of computers, infrastructure, and information systems by placing the bug within the larger contexts of infrastructure renewal, public works, and technological literacy.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.048 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| 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".