Links Between Terrorism, Organized Crime and Crime: The Case of the Sahel Region
Bibliographic record
Abstract
Many observers hold that terrorist groups and transnational criminal networks share many of the same characteristics, methods and tactics. There are many examples cited to demonstrate these observations are not coincidental, but indicative of a trend: a trend that is a growing threat to the security interests of many nations. We propose that the intersection of criminal networks and terrorist organizations can be broadly grouped into three categories – coexistence (they coincidentally occupy and operate in the same geographic space at the same time), cooperation (they decide that their mutual interests are both served, or at not least severely threatened, by temporarily working together) and convergence (each begins to engage in behavior(s) that is/are more commonly associated with the other). The activities of these types of organizations in the Sahel region of Africa provide examples of all three categories of interactions. This perceived threat has prompted action and policy choices by a number of actors in the sub-region. But this assessment might not be accurate and may, in fact, be an attempt to force an extra-regional, inappropriate paradigm upon a specific situation and set of circumstances where they do not apply.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".