Bibliographic record
Abstract
With its seven peacekeeping operations deployed in the African continent, the United Nations peacekeeping seeks to maintain peace and security by helping African states create conditions for sustainable peace. As COVID-19 has exposed the international system’s vulnerability, this analysis seeks to explore what Peacekeeping looks like in the COVID-19 era. By drawing on news articles, reports, and United Nations press releases, this account also examines the challenges faced by peacekeepers in Sub Saharan Africa, a region well known for violent conflicts and warfare. It is interesting to note that peacekeeping in the COVID 19 era appears to have struck a balance between protecting people's health, ensuring civilians protection from threats of physical violence, and taking gender dynamics into account. However, operational changes in peacekeeping missions resulting from COVID-19 seem to have a serious effect on missions and troops and might raise severe implications for the future of peacekeeping in Africa. APA Citation Makosso, A. M. (2020). United Nations peacekeeping operations in the era of COVID-19. The Journal of Intelligence, Conflict, and Warfare, 3(2), 1-17. https://journals.lib.sfu.ca/index.php/jicw/article/view/2378/1812
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".