Bibliographic record
Abstract
Unfree labor powered pastoral economies and agricultural societies in eighteenth- and nineteenth-century Central Asia. Historians have noted the presence of slaves in the region, but until now we have lacked a dedicated study on the trade’s scale and importance. In Slavery and Empire in Central Asia, Jeff Eden reveals that likely hundreds of thousands, primarily Iranian, slaves labored in Central Asia’s fields, homes, and workshops. The author steers our attention away from the small but significant number of Russians captured and sold as slaves and argues that the emancipatory discourse that accompanied the tsarist government’s conquest of the region concealed a more tactical approach to such a widespread phenomenon. Slaves were an accepted part of Central Asian society, supported and governed by religious edicts issued by Sunni Hanafi leaders. Shiʿi Iranians, mainly captured in raids or in warfare against a weak Qajar Empire, gained little empathy from Sunni Central Asians; only their skills might allow paths for limited social mobility within slave worlds. Even so, as geopolitics and balances of power among Turkmen tribes bordering Iran shifted in the late nineteenth century, slavery gradually dissipated as a factor in Central Asian life.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".