Challenging “Demon Superstition”: The Queen’s Bounty and Colonial Campaigns Against Triplet Killings in Onitsha Province, 1939–1956
Bibliographic record
Abstract
On 30 June 1939, Dr Bartley, a medical doctor at one of the missionary hospitals in Onitsha Province, recorded the distress of a woman who, after giving birth to triplets, tearfully questioned her humanity by asking, “Am I a dog”? Across colonial southeastern Nigeria, the birth of more than one child at a time was considered a sacrilege, leading to social ostracisation and deep-rooted stigma. Mothers of multiple births faced not only humiliation but also the pressure to abandon their newborns into the “ajo ofia” (evil forest) to avert divine retribution for supposedly defiling the land. While previous scholarship has illuminated the prevalence of twin killings in colonial Southeastern Nigeria, this article expands the conversation to include cases of triplets. Drawing on anthropological essays and previously unexploited archival documents, the article demonstrates how colonial authorities employed the Queen’s Bounty as a strategic tool to dissuade the practice of “demon superstition” in Onitsha Province. By examining individual cases, we analyse the complexities of this historical phenomenon and trace the enduring impact of the Queen’s Bounty on local perceptions of multiple births and colonial authority.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".