Is the <i>SlaveVoyages</i> database useful for scholars of slave trading in the wider Indian Ocean World?
Bibliographic record
Abstract
Assessing the differences between scholarly collaboration on slave trading in the Atlantic World, on the one hand, and similar activities in the wider Indian Ocean, on the other, needs to begin with an assessment of the relative importance of slave trading in the two oceans. Both oceans saw a maritime slave trade that drew heavily on sub–Saharan Africa. But while almost all captives arriving in the Americas came from Africa, in the Indian Ocean World there was a significant, probably majority, traffic in non-Africans, especially if one includes the South China Sea, as indeed most assessments of the Indian Ocean World slave trade do. Focusing on Africa alone initially, scholars who have made their name in the Atlantic World have tended to support the idea that the combined numbers of the Sahara Desert and Indian Ocean slave trade over two millennia were about the same as the volume of the transatlantic slave trade in its 360 years of existence.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.022 |
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".