Smearing time: Critical temporality and corporate ontology
Bibliographic record
Abstract
Since 1972 a leap second has been introduced into global time standardization systems, due to the discrepancy between Coordinated Universal Time and International Atomic Time. Until recently, the leap second has been a consensual, if mildly uncanny adjustment, a para-governmental temporal wobble. Google's explanation of its actions with regard to the insertion of a leap second smeared into its Network Time Protocol servers is couched in terms of a period extending initially over 20 h, ultimately reaching 24 h. Google is intent on taking ownership of the smear and transducing it into a technologically stabilised change. Although there are a number of different strategies of smearing time, Google advocates for its standard smear that it wants other digital giants like Bloomberg, Amazon and Microsoft to replicate. In this paper we first analyze Google's temporal strategy in terms of its affinities and departures from the classical view of time in Aristotle's core considerations in the Physics Book IV, in terms of a consonant enumeration but in our example at variable speeds/intervals, and then in terms of Wolfgang Ernst's conception of time-critical media. Leap seconds conform to Ernst's sense of kairotic time, an auspicious micro-moment that is both techno-mathematically pre-defined and decisive for ensuring operationality. Google executes smeared time-critical processes but wants to establish mastery over the measurement and manipulation of humanly imperceptible microtemporal events by inhabiting temporal ontology itself, proposing its practice, based on misleading its servers, as a model for other digital hegemons.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".