The Monster in the Corner of the Map: Russian Visitors Describe Nature on Sakhalin Island (1850–1905)
Bibliographic record
Abstract
Abstract This article examines evolving constructions of nature on Sakhalin Island in late imperial Russia, emphasising changing Russian views of not only the island, but of science, modernisation, mankind's power over nature and the borders of the empire. From a European land of plenty in the 1850s, welcoming to its Russian visitors, after a quarter-century of penal colonisation, the island had become a monster devouring its prey. This article argues that contradictory and evolving descriptions of Sakhalin's nature reflect tensions Russians faced in a modernising world, as they questioned the relationship between mankind and nature; the reliability of science; and the correct borders of their state. In the 1850s, Sakhalin seemed normal and bountiful, a gift to Russia, while two decades later, it was wealthy but hostile, although, with science, Russians could prevail. By the 1890s, that was called into question, and the island was portrayed as not only hostile, but foreign, desolate and unsubmissive to science; while activists of the early twentieth century reimagined it as abundant, comprehensible and vital to the empire. The image of Sakhalin as hostile and unintelligible prevailed, reflecting a widespread disillusionment with Western modernity. In 1905, Russia surrendered the southern half of the island to Japan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".