Bibliographic record
Abstract
This chapter begins with a discussion of the increasing empowerment of nonstate actors to challenge nation-states for commerce and security. One of the central trends is the operational coordination of organized crime and terrorist entities. They tend to share the same pipelines and at times some of the same personnel since both have a need for similar specialized skills. We witness the emergence of the third generation of guerillas, described as leaderless or horizontal in structure, global, and highly adaptable to urban areas. Crevald’s position on guerilla wars is discussed—he asserted that states can never effectively combat guerilla wars. We then discuss the many ways in which transnational criminal organizations and terrorists are working together in the current era. From there, we assess the roles of criminalized states and “black holes,” geographical areas not controlled by state-based security entities. We discuss how global warming works to enhance the strengths of nonstate actors, while at the same time undermining or limiting state security. This chapter closes with a discussion of the security changes likely to be associated with an ice-free Arctic Ocean. We look at changes already occurring, and assess the roles played by the United States, Canada, Russia, and China. All are in exploration stages for previously inaccessible resources, and most are increasing their military presence across the region.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".