Export Controls as Innovation Marketing? Sociotechnical Imaginaries in the Ringfencing of Quantum Technologies
Bibliographic record
Abstract
Why are a host of states, such as the United States, Canada, the United Kingdom, France and the Netherlands, imposing export controls on quantum computers with technical specifications (e.g. 2000 qubits) that are not yet realisable? No full-fledged ‘useful’ quantum technology (QT) exists yet; instead, the regulatory object of export controls is the network of technological artefacts (equipment, prototype, proof-of-concepts), people and labs (the ‘assemblage’ of quantum innovation) endeavouring to make quantum a reality. Thus, export controls serve mainly as a tool of knowledge regulation over critical knowledge and R&D exchanges taking place to realise the quantum ambition. This article contends that it is not the material reality of quantum innovation – which is still mired in major engineering challenges – that informs export control efforts surrounding QT, but rather the ‘sociotechnical imaginary’ of quantum that serves as the ‘muse’ for law- and policy-makers. Quantum imaginaries are pivotal to understanding the rationales of QT export controls and the narratives in which they are entrenched. It is not necessarily the ‘2000 qubits’ in and of themselves, their technical (non-)feasibility or (non-)realisability, but rather the imaginaries told and believed about their technological possibilities and power that are decisive in the ringfencing performed by export controls on QT.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.083 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".