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Record W4416276326 · doi:10.1163/24683302-bja10101

A Series of Unfortunate Events: “First Battles” of the Nigerian Contingent in the Cameroon Campaign (1914–1916)

2025· article· W4416276326 on OpenAlexaff
Adeboye Tinubu

Bibliographic record

VenueInternational Journal of Military History and Historiography · 2025
Typearticle
Language
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsColonialismBattleGermanFrontierPower (physics)Work (physics)

Abstract

fetched live from OpenAlex

Abstract The outbreak of the First World War marked the first time that Nigerian colonial soldiers were deployed against troops of another colonial power on the continent. British-ruled Nigeria’s newly unified army confronted its first combat challenge in Cameroon against the German colonial Schutztruppe . The initial confrontations between British colonial Nigerian soldiers and German Cameroon forces during the early stages of the First World War in West Africa resulted in a series of resounding defeats for the Nigerians. Nigeria Regiment units gathered at Yola, Maiduguri, and Ikom were tasked with defending the Nigerian borders from German incursions and launching offensives into Cameroon against critical frontier towns like Garua, Mora, and Nsanakang. Using the “first battles” principle articulated by military historians Charles Heller and William Stofft, this study concludes that inadequate preparation, failure of intelligence, and indecisive leadership by British officers led to a series of first battle defeats for British Nigeria’s colonial units. Furthermore, this work argues that the lessons learned from the defeats led to organisational and tactical shifts by the British Nigerian units and ultimately contributed to the expulsion of the Germans from Cameroon.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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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