Bibliographic record
Abstract
Abstract By the time the transatlantic slave trade ended in 1866, around 12.5 million enslaved Africans had been forcibly embarked from hundreds of coastal slave-trading regions stretching from Senegal to Mozambique to populate most regions in the Americas from Newfoundland to Patagonia. For over three centuries, these displaced people were vital for the western hemisphere’s European conquest, settlement, and economic growth. Every major European nation and some of its colonies, such as Cuba, Brazil, and the United States, outfitted slaving expeditions. Based on the Trans-Atlantic Slave Trade Database (Voyages) and the most recent scholarship, this article addresses a selection of demographic topics which have remained at the core of the historiographical debates on the transatlantic slave trade, such as the number of enslaved Africans carried to the Americas, the mortality rate during the middle passage, African regions of provenance and destination, national carriers in the transportation of Africans, shipboard rebellions, and the gender and age of the captives. These demographic parameters, such as embarkation and disembarkation regions, mortality rate, the nationality of the carriers, or shipboard revolts, were interdependent and experienced significant temporal and regional changes.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".