Setting the Stage for Kru Wage Labourers in West Africa and the Americas: Tracing Early Kru-Portuguese Relations from the Sixteenth Century
Bibliographic record
Abstract
Tracing the Kru Diaspora from the coastal and interior regions of Liberia to Sierra Leone as wage labourers in British commercial and military workplaces in the late eighteenth century and as labourers in British Guiana in the mid-nineteenth reveals the active role Africans have played in British trade and calls for a rethinking of African agency in the development of global capitalism. Their case remains an early example of the “outsourcing” wage labour model that has come to dominate work environments in the 21st century. / The question arises: Why were the Kru largely exempt from enslavement and instead hired by numerous powers including the Portuguese, British, French, Germans and Americans to work on trading and military vessels? Based on oral traditions, interviews and primary sources I argue that the coastal Kru’s traditional occupation as seamen engaged in fishing and trading, the rugged coastal terrain and currents that characterized the Windward Coast, and the fluid nature of their politically decentralized communities all combined and enabled the formation of strong trading ties between the Kru and the Portuguese as early as the 16th century. My paper will shed new light on early Kru-Portuguese relations and examine the themes of Kru identity formation and economic 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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".