Bibliographic record
Abstract
Cyberspace is a unique human-made environment on which global society is increasingly dependent for its well-being. At the same time, states and non-state actors are engaged in detrimental cyber activity that can threaten to transform this special environment into just another battleground. Diplomatic efforts to develop international “norms of responsible state behavior” have not kept pace with growing military cyber security capabilities. A Sino–Russian initiative for an “International Code of Conduct for Information Security” has problematic aspects and could prove divisive if brought before the UN General Assembly for adoption. A series of reports by UN Group of Governmental Experts have generated some important general conclusions and positive recommendations for confidence building measures, but they remain only proposals. There is a need for more Western leadership in ensuring that expert recommendations are transformed into state commitments if the peaceful nature of cyberspace is to be preserved.\nPublished in the Simons Working Papers series as: Meyer, Paul. Seizing the Diplomatic Initiative to Control Cyber Conflict. Simons Papers in Security and Development, No. 45/2015, School for International Studies, Simon Fraser University, Vancouver, September 2015.
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 | 0.008 | 0.007 |
| 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 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".