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Record W2112674244 · doi:10.5787/26-2-243

DIE SOMALILAND BURGHER CONTINGENT

2012· article· en· W2112674244 on OpenAlexaboutno aff
Gerhard Genis

Bibliographic record

VenueScientia Militaria South African Journal of Military Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsDemobilizationVictoryAncient historyHistoryPolitical scienceGeographyEthnologyEconomic historyLawPolitics

Abstract

fetched live from OpenAlex

The Somaliland Burgher Contingent, consisting of Afrikaans and English speaking South Africans, was the first South African volunteer unit who fought on foreign soil. Volunteers hailed not only from South Africa but from Britain, Rhodesia, America, Ireland, Scotland, Canada and Austria as well. This truly cosmopolitan unit fought during the Third Expedition (1902-1903) in Somaliland against Mohammed Abdulle Hassan (1856-1920) - the so-called Mad Mullah. During this expedition the South Africans fought side by side with British troops of the King's Royal Rifle Corps, Indian troops and the King's African Rifles whose members consisted of Somalis, Sikhs, Sudanese and Yaos (Africans). These troops endured many hardships during the campaign: they had to march in extreme heat in thick bush country with meager rations and water. The Somaliland Burgher Contingent was involved in skirmishes with the Dervish forces of the Mullah but not in the disasters of Gumburu and Daratoleh during which two British forces were cut up. The Third Expedition was a military failure for the British and a victory for the Mullah who wanted to rid his country of the British infidel. The Mullah resisted British and Abysinian intrigues in his country until his death in 1920. Apart from the campaign, the composition, character, mobilization, demobilization and compensation of the Somaliland Burgher Contingent as well as the reasons why volunteers joined up, are discussed.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.298
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 designQualitative
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

Citations1
Published2012
Admission routes1
Has abstractyes

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