“Halfway between Peking and Western Europe”: Imperial Knowledge Creation and the Geography of Buddhism in Sir Aurel Stein’s Last Major Maps of China
Bibliographic record
Abstract
Although Sir Aurel Stein (1862-1943) is best known today for acquiring medieval manuscripts from Dunhuang, China, in the early twentieth century, he was also a life-long cartographer, and his map-making processes tell us much about his reliance on, and contributions to, history, philology, archaeology, geography, hydrology, anthropology, and Buddhist studies. This dissertation, “Halfway between Peking and Western Europe”: Imperial Knowledge Creation and the Geography of Buddhism in Sir Aurel Stein’s Last Major Maps of China, peels back the layers of Stein’s maps of northwestern China to uncover the often-asymmetrical exchanges that provided geographic—and other—information for Stein. I locate Stein in the context of elite imperial academics and military officers, showing how he fashioned himself as a scientist while producing geospatial intelligence for British officials wary of Russian and Japanese interests in the region. I also demonstrate how he relied on a wide range of intermediaries in the field, including surveyors, guides, interpreters, “local experts,” so-called “treasure-seekers,” and the record of the pilgrim Xuanzang (602-664 CE), all of whom shaped the knowledge Stein collected and published. Their influence is nowhere felt more than in a surveyor’s flaw affecting a quarter of Stein’s final maps of the region, and here I use archival documents and Geographic Information Systems (GIS) to explore the role this flaw played in Stein’s cartographic career. These investigations converge in my analysis of Stein’s engagement with the “wandering lake” Lop-nōr and the nearby Lop Desert, which shows how Stein adapted the concept of “desiccation,” a theory that blamed desertification on the misuse of land by local populations, into a narrative capable of synthesizing data he acquired from such varied methods as hydrology, surveying, archaeology, historical toponyms, and cranial measurements of the living and the dead. This dissertation thus argues for the necessity of Stein’s cartography in understanding this major public figure, but it also unsettles the idea that his maps were objective windows onto Central Asia. His maps instead depicted what he called the “dead heart of Asia” as a frontier with a rich archaeological past, a conflicted present, and an uncertain future.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".