The United States and Africa: Cybernetic Foreign Policy, Continental Decline
Bibliographic record
Abstract
A broad divergence between rhetoric and reality characterizes U.S. Africa policy through time. Given Africa's relatively marginal place in U.S. global strategy, a policy of minimum risk/minimum expense is considered sufficient for American interests. Africa is important only in so far as instability on the continent assists America's enemies and contributes to global terror. Secondarily, Africa is seen to be an untapped market for U.S. goods and services, but this is more potential than real. In consequence, a relatively unoriginal package of aid 'products' is available to the continent: money for peacekeeping, HIV/AIDS, good governance, education and health, regional peace and security. Where problems increase or persist, a bit more money is made available. This I label “cybernetic foreign policy making”, a heuristic meant to impart a sense of the virtually automatic way in which U.S. policy reacts to events on the continent. It is the central argument of this essay that such policy making and practice does more harm than good. This is because the underlying assumption - that liberal politics and economics can be pushed simultaneously - runs against historical fact. There are positive policy options available - debt forgiveness, access to U.S. markets - but these run counter to American grand strategy. U.S. Africa policy therefore contributes to continental instability, but as long as this is 'low level', a policy based largely on containment is all that should be expected.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".