The enslaved, the Fante, and the governors: rediscovering the Detached Papers of the Company of Merchants trading to Africa
Bibliographic record
Abstract
Abstract This article explores the Detached Papers of the Company of Merchants Trading to Africa, an underused sub-series within The National Archives’ T 70 collection. These records offer a unique and detailed insight into the British administration of West African forts, and the lives of enslaved people forced to work in these fortifications. While the Royal African Company has been the subject of extensive scholarship, the Company of Merchants – its successor – remains understudied. Through letters, minute books, fort lists, and financial records, the Detached Papers, recently catalogued at item level for the first time, provide a critical and untapped source on the eighteenth-century Atlantic world, revealing overlooked narratives of local relationships, familial networks, and the operational structures that underpinned the transatlantic trade in enslaved Africans. This article focuses on three key areas to exemplify the importance of these records for future research: the cultural and political life of the Fante; the networks and influence of Company Governor Richard Miles; and the identities and experiences of enslaved people in British-controlled forts. By engaging with the fragmentary nature of these records, the article interrogates archival silences to surface the submerged histories of exploitation, agency, and survival within the archives of enslavement.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".