Making Digital Territory: Cybersecurity, Techno-Nationalism, and the Moral Boundaries of the State
Bibliographic record
Abstract
Drawing on an analysis of German national cybersecurity policy, this paper argues that cybersecurity has become a key site in which states mobilize science and technology to produce state power. Contributing to STS work on technoscience and statecraft, I develop the concepts of ‘territorialization projects’ and ‘digital territory’ to capture how the production of state power in the digital age increasingly relies on technoscientific expertise about information infrastructure, shifting tasks of government into the domain of computer scientists and network engineers. The notion of ‘territorialization projects’ describes states’ ongoing struggle to mobilize science and engineering in order to transform globally distributed information infrastructure into bounded national territory and invest it with patriotic meaning: making ‘digital territory.’ ‘Digital territory,’ in other words, is nationalized information infrastructure: it includes building and monopolizing infrastructure as well as normative ideas about nation — who is a digital citizen, and who isn’t; or what constitutes ‘good’ and ‘bad’ digital citizens. Nationalizing information infrastructure and placing statecraft into the hands of scientists and engineers might indicate an emerging form of ‘techno-nationalism’ — a combination of nationalist and technocratic tendencies — raising urgent questions for STS scholarship to investigate the consequences of territorialization projects for justice, democracy, and civic life.
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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 | 0.002 | 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 | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".