Bibliographic record
Abstract
Abstract Before the middle of the 2nd millennium ce, most Africans lived in relatively small-scale societies. An important role in the development of more complex societies was played by using slaves. Even among the least complex societies, “big men” developed entourages of kin, clients, pawns, and slaves. Out of struggles with other “big men,” chiefdoms and early states evolved. States gradually became larger, more complex, and more centralized. The most complex states developed in the Sudanic belt that stretched across Africa just south of the Sahara. Islam justified enslavement, horses facilitated it, and slaves were a major item of trade. They were not only menial slaves doing agricultural and artisanal labor but also soldiers and administrators. With the development of the Atlantic slave trade, this process was replicated but on a much larger scale. The 17th century saw the emergence of a series of powerful slave-trading and slave-using states. The end of the transatlantic slave trade in the 19th century did not end the slaving cycle but, instead, diverted slaves to producing commodities for export. If anything, slaving increased as did the use of slaves, particularly in states led by or created by warlords. The armies that conquered Africa at the end of the century were also largely slave armies, and many former slaves took on subaltern roles in the colonial administration. Slave women in harems also played a role as the mothers of princes and sometimes as administrators within royal households.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".