<i>Robinson Crusoe</i> and the Slave Trade: A Treatise Against Adventure
Bibliographic record
Abstract
The breakout of the Muslim slave trade into the Atlantic in the early seventeenth century coincides with the dramatic escalation of the European slave trade in Africa. In the course of his adventures, Robinson Crusoe is enslaved by Arabs precisely as he seeks to enslave Africans. What enabled both these two moments of expansion in human trafficking, the critical link between them, was technology. And so while my general aim in this essay is to recontextualize these developments in order to understand why there was so little articulate opposition in seventeenth-century Europe to the resurgence of the slave trade, my immediate aim is to understand how in the case of Robinson Crusoe the relationship between technology and contingency contributes to this moral blindness. My argument falls into two parts: the first focuses on technology and the way its limitations or failures afford Crusoe a spiritual awakening or renewed religious perspective, and the second on the way that awakening or moment of grace, somewhat surprisingly, actually serves to protect the slave trade. It does so because it shifts attention from the morality of slavery itself to a subsidiary moral debate over the rival claims of thrift and adventure in the pursuit of a good life—that is, a life that is both virtuous and commercially successful.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".