Third-party countries in cyber conflict: Public opinion and conflict spillover in cyberspace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The transnational nature of cyberspace alters the role of third-party countries (TPCs) in international conflict. In the conventional environment, military operations are primarily confined to the boundaries of the combatants or a designated war zone. However, during cyber conflicts, operations may occur on the digital infrastructure of states not otherwise involved in the dispute. Nevertheless, within the cyber conflict literature, little is said about TPCs who, by virtue of interconnectivity, may find themselves involved in a conflict not of their own making. Consequently, we examine the political and diplomatic hazards of cyber operations involving these actors. Through survey experiments involving participants from the United Kingdom and Canada, we assess the public opinion impact of an offensive cyber operation’s revelation on a TPC population. We find that while these incidents are viewed negatively, prior authorization and the involvement of an ally reduces this tendency. Such conditions lead the public to perceive these operations as corresponding with their national interest while suppressing fears of the possible consequences following their indirect involvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 it