Infrastructuring public-private relations: Big Tech, the Ukraine War and implications to security governance
Bibliographic record
Abstract
This paper demonstrates how an infrastructural lens offers novel ways of interrogating public-private relations in International Relations (IR). Advancing the idea that infrastructures mediate public-private relations, the paper argues that an infrastructural approach is well positioned to capture both the productivity and ambiguity of public-private boundary drawing in international politics. It deploys the infrastructural approach to examine Big Tech and state relations in the war in Ukraine. The article shows how infrastructural mediation of public-private relations enables analysis of how Big Tech companies matter to international politics, moving beyond debates about state decline or corporate dominance on the one hand, and infrastructure as an external driver of change that allows for a neat separation of states and Big Tech and politics and technology on the other. The analysis demonstrates how sovereignty, geopolitical decision-making and national security knowledge are contingent upon infrastructurally mediated Big Tech company and state relations. The article thereby points to how infrastructures are at the core of expressing and realizing what makes both states and Big Tech companies, offering new avenues for understanding and examining public-private relations in international politics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".