Bibliographic record
Abstract
Sanctions are frequently applied by the UN Security Council (UNSC) as well as regional organizations. While the objectives sought often vary, a frequent commonality is that they target African states. Indeed, Africa is the most frequently targeted continent by the UNSC and regional organisations including the African Union, Economic Community of West African States and the European Union. However, little attention has been paid to the confluence of this sanctions activity by these different organizations. This article seeks to address this gap in the research. While the UNSC continues to focus on sanctioning to end hostilities, the regional organizations have assigned themselves unconstitutional changes to government as the principal reason to sanction African states. Drawing on data from the Targeted Sanctions Consortium (TSC), this article suggests that: 1) regional organisations are leading UNSC activity more often than is appreciated in the literature; 2) the UNSC has of late been expanding its sanctioning activity to consider issues of democracy and good governance; 3) the UNSC uses sanctions to endorse the activity of African regional organizations to deal with crises on the continent; and 4) UNSC and regional sanctions are intimately tied to crisis management in Africa.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".