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Record W55811929 · doi:10.3138/cjh.41.1.1

Kenya’s “Forgotten” Engineer and Colonial Proconsul: Sir Percy Girouard and Departmental Railway Construction in Africa, 1896-1912

2006· article· en· W55811929 on OpenAlexvenueaboutno aff
John Mwaruvie

Bibliographic record

VenueJournal of History · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsProtectorateMaasaiColonialismGovernorHistoriographyHuman settlementHistoryAncient historyPolitical scienceEconomic historyArchaeologyEthnologyEngineeringTanzania

Abstract

fetched live from OpenAlex

Sir Percy Girouard remains largely unknown not only in his native Canada, hut also to the travelers who daily use the railways he constructed in Sudan, Nigeria, and Kenya. In Kenya, Sir Percy Girouard is associated with the debacle of the Second Maasai Agreement of 1911, which led to their forceful removal from the fertile Laikipia plateau to semi-arid Ngong. The Maasai land alienation policy ended his spectacular career as governor of East Africa Protectorate (Kenya) in 1912. Consequently, he appears in Africanist historiography as a failure. However, there is another side of Girouard that has escaped the attention of researchers and represents a more lasting legacy than that of other reputed colonial governors in Africa. His major contribution was in formulating and implementing railway policies for both military and commercial transport. Furthermore, as British High Commissioner for Northern Nigeria he instituted a cost-effective departmental railway policy during the construction of the Baro to Kano Railway. As governor of Kenya, Girouard was instrumental in initiating railway extension policy that led to construction of the Nairobi-Thika and Konza-Magadi railways. This article establishes Sir Percy Girouard as a great contributor to railway development in Africa; his achievements deserve mention in any discussion of British colonial development.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.645
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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.

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

Citations1
Published2006
Admission routes2
Has abstractyes

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