A research study towards the improvement of human security and peace in cyberspace
Bibliographic record
Abstract
The ever-increasing dependence on the internet for the performance of human functions blurs some distinctions between physical and virtual worlds. Tasks like the performance of surgical procedures in hospitals depend on digital tools for efficient and effective health delivery. Still, society is witnessing both new forms of violence and the transposition of violent forms from the physical world to the cyber world. Cyberspace provides an easier option for harm to be caused to individuals because both state and non-state actors can extend their actions beyond their physical reach; commercial spyware is being deployed against political opponents in many countries. Without obtaining express consent, some organizations may be involved in trading the personal data of clients for business gains. During all these, the search for cyber peace has become difficult because of divergent views on what constitutes cyberviolence and the potency of cyber weapons. This research seeks to integrate discussions among scholars and the perspectives of some cybersecurity practitioners on cyber peace and violence. The decision to interrogate cyber peace and violence from the perspectives of cyber-security practitioners will contribute towards building some stability for these evolving concepts within the peace and conflict doctrines. Four cybersecurity professionals were interviewed on the subject. The basic human needs theory, human rights, and social justice theories are used to interrogate the understanding of cyber peace and violence. The results indicate that cyber harms targeting both state and non-state actors and installations should be considered in conflict analysis. This approach helps to enhance the concept of positive and negative cyber peace as possibilities.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".