Bibliographic record
Abstract
Purpose The empirical record of cyberattacks features much computer crime, espionage and hacktivism, but none of the major damage feared in prevalent threat narratives. The purpose of this article is to explain the absence of serious adverse consequences to date and the durability of this trend. Design/methodology/approach This paper combines concepts from international relations theory and new institutional economics to understand cyberspace as a complex global institution with contracts embodied in both software code and human practice. Constitutive inefficiencies (market and regulatory failure) and incomplete contracts (generative features and unintended flaws) create the vulnerabilities that hackers exploit. Cyber conflict is a form of cheating within the rules, rather than an anarchic struggle, more like an intelligence-counterintelligence contest than traditional war. Findings Cyber conflict is restrained by the collective sociotechnical constitution of cyberspace, where actors must cooperate to compete. Maintenance of common protocols and open access is a condition for the possibility of attack, and successful deceptive exploitation of these connections becomes more difficult in politically sensitive situations as defense and deterrence become more feasible. The distribution of cyber conflict is, thus, bounded vertically in severity but unbounded horizontally in the potential for creative exploitation. Originality/value Cyber conflict can be understood with familiar political economic concepts applied in fresh ways. This application provides counterintuitive insights at odds with prevalent threat narratives about the likelihood and magnitude of cyber conflict. It also highlights the important advantages of strong states over the weaker non-state actors widely thought to be empowered by cyberspace.
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.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".