A history of child trafficking in southeastern Nigeria, 1900s-1930s
Bibliographic record
Abstract
Southeastern Nigeria underwent significant economic, political, and social change as a result of European conquest. The transatlantic slave trade, the expansion of the legitimate trade in palm oil and the subsequent need for domestic labor contributed to a long history of human trafficking. The existing continuities between the transatlantic slave trade and the ways in which colonial authorities and Igbo, Ibibio, Efik and Ijaw (Ijo) populations mobilized juvenile productive and reproductive labor is at the center of this study. Child dealing supported domestic labor needs at a time when the legality of colonial ‘slavery’ was in flux. Nigeria’s steep economic decline of the 1920s and 1930s caused a dramatic increased in child pawning, stealing and dealing. This dissertation examines the porosity between the institutions of pawnship, slavery, child (girl) marriage, panyarring, serfdom, clientelism, and servanthood. The transformation of child pawning, a family strategy that used children’s labor as collateral for loans, is highlighted as an institution that allowed slippages from ‘pawn’ to ‘slave’ as the procurement and post-slavery exploitation of children became an important local system of attaining child labor. Many parents pawned children in order to pay colonial taxes. Men pawned girls to raise the bride price needed for their own marriages and child dealers stole and sold children to add to their personal wealth. This study also examines the actions of Nigerian, European, American and Canadian women who attempted to challenge colonial policies at a time when the welfare of poor Nigerian children decreased and colonial economic demands increased. The decline of Nigerian children’s livelihoods occurred at the same historical moment that American, European and Canadian activists sought to increase the protection of children globally. This work also disrupts assumptions about categorizing certain groups as primary trafficking ‘victims’. Members of the League of Nations and similar groups concerned with the rights of women and children often focused on women and children as specific groups needing protection in colonial Nigeria. However, my work shows that women and children participated in human trafficking for the same reasons men did⎯they wanted to enhance their own financial and social security.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".