Resettlement, Resistance, and Coastal Niches on the Chukchi Peninsula
Bibliographic record
Abstract
As were other regions of Russia’s North, Chukotka (Chukotskii avtonomnyi okrug) was subjected to dramatic changes during the last century. Among the major long-lasting impacts for the Chukchi and Siberian Yupik Indigenous populations was a state-implemented village relocation policy that deemed dozens of historic settlements “unprofitable”, thus subject to forced closure and resettlement. Traumatic loss of homeland, the curbing of native patterns of (maritime) mobility, and the vanishing of traditional socioeconomic structures sent devastating ripples through the fabric of Indigenous communities, with disastrous results on societal health. To explore the intricate relationships between state-enforced resettlement and landscape interaction, particularly the perception and utilization of the environment, it is critical to look closely at Chukotka’s coastal environment. The article argues that the unique coastal landscape of Chukotka has influenced—while mitigating—the effects of the forced relocations. Improvised design and the reclaiming of formerly closed settlement sites play a paramount role here, with the reoccupation of old settlement niches representing a reconnection with a lost relationship to the littoral environment. The contemporary inhabitation and utilization of formerly closed villages show how the coastal landscape represents not only a “reservoir” in an ecological sense, but also a littoral reserve by providing the space for alternatives outside the congregated communities. Displacement destroys the sense of community, but in a reverse logic, a sense of community can also be established through renewed emplacement. The creation of autonomous social spaces is therefore part of an ongoing spatial resistance that actively uses the ecological niches of a coastal landscape to counter the long-lasting and detrimental effects of state-enforced resettlement policies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".