Introduction: African Cities and Urban Slavery in Historiographical Perspective
Bibliographic record
Abstract
This introductory essay explores the rich historiography lying at the intersection of African urban and African slavery studies. How does the study of slaves, former slaves and those of slave descent in urban environments help us understand emancipation in Africa? How have those experiences of historical and contemporary emancipation shaped African cities? Case studies from Gambia, Mauritania, Niger, Tanzania, and Madagascar address these questions. Contributors question long-held assumptions about cities providing autonomy, anonymity, and prosperity to those of slave origin. They suggest that interconnections between the rural and the urban are both material and ideological; moreover, memories and traditions travel the same migration paths as people. Thus, life histories tracing individual trajectories are key to revealing the humanity of urban slavery. As important as recent cultural studies are, however, labor—what people do, why they do it, and who they do it for—remains central to the urban “post-slave” experience.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".