Cyber Espionage and International Law. By Russell Buchan. Oxford: Hart, 2018 (Book Review)
Bibliographic record
Abstract
In 2015, then US President Barack Obama referred to cyberspace as the “new Wild West” — vast, lawless, and without a sheriff in sight. Given these qualities, it is unsurprising that actors leverage cyberspace to perpetrate crime, terrorism, foreign influence, and espionage with increasing effectiveness. Meanwhile, the international community has struggled to find common ground on the application, let alone enforcement, of international law in cyberspace. While there have been a number of high-level commitments made by allied states to work together to develop “norms of cyberspace,” the prominent and decade-long effort of the United Nations Group of Governmental Experts to address state behaviour in cyberspace collapsed in 2017 due to a lack of consensus. Two separate and open-ended working groups have since taken its place; one effort led by the United States, the other by Russia. In his text, Cyber Espionage and International Law, Russell Buchan not only takes on the notion that cyberspace is a lawless domain, but he also challenges the oft-repeated assertion that state-sponsored espionage is lawful under international law.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".