Rethinking Secrecy in Cyberspace: The Politics of Voluntary Attribution
Bibliographic record
Abstract
Cyberspace affords actors unprecedented opportunities to carry out operations under a cloak of anonymity. Why do perpetrators sometimes forgo these opportunities and willingly claim credit for attacks? To date, the literature has done little to explain this variation. This article explores the motivations behind voluntary credit-claiming for the two main actors in cyberspace: states and politically motivated nonstate actors. We argue that states are most likely to claim credit for their operations and to do so privately when the goal is to coerce an opponent. Nonstate actors tend to publicly claim credit for their attacks in order to showcase their capabilities, influence public opinion, and grow their ranks. We use case narratives to assess the plausibility of our argument and find strong support. This article places cyberspace operations in conversation with the larger literature on secrecy in international relations and advances a common framework for understanding how both states and nonstate actors operate in this evolving domain.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".