Bibliographic record
Abstract
In August 2021, the United States withdrew the last of its troops from Afghanistan, ending its military presence there after nearly 20 years. This paper uses the Systems theory to examine the effects of this withdrawal on human security in Africa. The military action in Afghanistan represented a massive global coalition effort. In addition to the United States, the 20-year period saw military forces and assets from the United Kingdom, Australia, Canada, the Czech Republic, France, Germany, Italy, Japan, New Zealand, Poland, Russia, and Turkey. The termination of this conflict is likely to have long-term effects on human security in Africa. These effects traverse personal and community security, economic security, environmental security, food security, health security, and political security, all of which fall within the larger domain of human security. This qualitative method-based paper uses the SALSA model to source and analyze information from a diverse set of literature. The key findings of this research are the likely increase in terrorism activities, the dangers posed by the increasing population of refugees, the problem of proliferation of sophisticated weapons and small arms, and increased economic challenges such as unemployment and poverty. The theoretical lenses provided by the Systems theory showed that Africa is a player within the global system. The identified effects require Africa to rethink its strategy and policy to decisively address them. Additionally, the silenced guns must translate to improved human security across the continent by refocusing the massive resources that were being used to sustain this campaign towards the creation of sustainable developmental programmes across the African continent. On her part, the continent must come up with strategies to address human security challenges, create employment, enhance efforts to fight terrorism, minimize the impact of refugees, eliminate drug abuse, and stop the proliferation of illegal arms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".