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Record W4408854005 · doi:10.1080/17532523.2025.2455792

Challenging “Demon Superstition”: The Queen’s Bounty and Colonial Campaigns Against Triplet Killings in Onitsha Province, 1939–1956

2025· article· en· W4408854005 on OpenAlexaff
James Akpan Ekah, Patrick Chukwudike Okpalaeke

Bibliographic record

VenueAfrican Historical Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsYork University
Fundersnot available
KeywordsDemonSuperstitionColonialismQueen (butterfly)HistoryGenealogyCriminologyAncient historyArtSociologyLiteratureBiologyArchaeologyZoology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.275
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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