Making everything ac-count-able: The digital twinning paradigm
Bibliographic record
Abstract
This editorial investigates the epistemic and media-theoretical significance of digital twinning as a decision-making practice. Digital twins purport to calculate futures based on sensor data in conjunction with generative AI, cloud computing, and Internet-of-things architectures; they shape institutional decisions and are used to make such decisions accountable. To illustrate this, examples from the logistics, transportation, and military sectors are contrasted to earlier simulations and described as “phenomenotechniques.” We argue that digital twins are recent expressions of a technocratic paradigm characterized by the imperative to make everything worldly “count,” datafying and modeling it within digital environments in real time for future predictions. Digital twins are thus performative agents in a network of feedback loops between humans, machines, environments, and algorithms. This article concludes with an overview of the special issue, placing digital twins in the phenomenological context of media that are seamlessly and simultaneously logistical, spatial, and transformative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".