Diamonds, risk and insurance practices in British Africa, with an emphasis on the Gold Coast and Tanganyika
Bibliographic record
Abstract
The literature on diamond mining in colonial Africa has focused more on labour, production and cartel-style price fixing than on how theft threatened the wealth generated from this industry. To mediate diamond loss, companies subscribed to financial insurance. This article introduces security-as-insurance to complement the received meaning of (financial) insurance and invites scholars to see security-as-insurance, risk, and value as interlocked. Drawing on sources from Ghana, Tanzania and Kenya, this article argues that once diamonds were imbued with financial value, the distribution of this value was competed for via claims over diamondiferous lands, diamond stealing, and consequently counter-theft security practices plus trans-empire (financial) insurance intended to protect the stones against risk. Overall, this article historically traces how diamonds gained value in Africa, the competing claims over diamonds following discoveries in commercial quantities, and the insurance practices enforced to protect against losses, particularly via theft in colonial Ghana and Tanzania.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".