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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".