Reparations for Africa: Infrastructure, Education, and Industry for Africa are Long Overdue Based on the Legal Concept of Unjust Enrichment
Bibliographic record
Abstract
This article suggests that a legal argument could be made out that Africa, in the hands of the Western sovereign nations over a period of three hundred years dating back to the 17th century, has been the victim of multivarite expropriations of resources and wealth in a case of unjust enrichment. The courts of English speaking countries usually ask: 1. Was there enrichment? 2. Was it at the expense of the victim? 3. Was in unfair? 4. Is there a good reason for it which might excuse it? 5. What remedy should be enforced? Once you have answered these questions and have determined a remedy is in order, one of the concepts brought to bear on the analysis is that if there is money owing, then compound interest is most usually applied, for obvious reasons. Here I suggest that because the sums owning are incalcuably high, a program of repayment in reparations by the relevant sovereign states in the form of infrastructure, education, and industry would be the only just way forward.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".