MétaCan
Menu
Back to cohort
Record W2980769445 · doi:10.1163/24680966-00301003

Martial Identities in Colonial Nigeria (c. 1900–1960)

2019· article· en· W2980769445 on OpenAlexaff
Timothy J. Stapleton

Bibliographic record

VenueJournal of African Military History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHausaIgboYorubaColonialismNigeriansEthosZuluAncient historyGender studiesHistoryGeographyPolitical scienceLawSociology

Abstract

fetched live from OpenAlex

Abstract In British colonial Nigeria, the military was more heterogeneous than previously thought and British ideas about “martial races” changed depending on local reactions to recruiting. In the early twentieth century British officers saw the northern Hausa and southwestern Yoruba, who dominated the ranks, as civilized “martial races.” The Yoruba stopped enlisting given new prospects and protest, and southeasterners like the Igbo rejected recruiting given language difficulties and resistance. The British then perceived all southern Nigerians as lacking martial qualities. Although Hausa enlistment also declined with opportunities and religious objections, the inter-war army developed a northern ethos through Hausa language and the northern location of military institutions. The rank-and-file became increasingly diverse including northern and Middle Belt minorities, seen by the British as primitive warriors and as insurance against Muslim revolt, enlisting because of poverty. From 1930, military identities in Nigeria polarized with uneducated northern/Middle Belt infantry and literate southern technicians.

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 score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of African Military HistorySame topicAfrican history and culture studiesFrench-language works237,207