Slaving and the Funding of Elite Status in Early Medieval Europe (ca. 800–1000 AD)
Bibliographic record
Abstract
In the last few years research on early medieval slavery has seen an ever-growing emphasis placed on long-distance slave trading, and on the raiding practices that fed this trade. There has been relatively little link-up between this and older historiography looking at slavery in terms of labour and social history: the slaves who moved and the slaves who stayed have largely been kept in separate conversations. The increased profitability of slaving, though, should lead us to expect a rise in the internal importance of slavery as well as in slave-raiding and trading to external markets. This is what we find in many of the regions most intimately associated with the trade. There were, however, profound regional differences in the profiles and forms of engagement in slaving activities across Europe. I suggest that Joseph C. Miller’s idea of slaving as a political strategy adopted by marginal players seeking to bypass normal forms of elite competition is helpful in thinking through the logic of these different responses to the opportunities and challenges presented by the slave trade: what motivated and constrained elite choices and possibilities? And what made slaving a more viable political strategy in some regions than in others?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".