Bibliographic record
Abstract
The city of Slavutych was built specifically to house the workers who would continue to work post-disaster at the Chornobyl Nuclear Power Plant (ChNPP) and their families. It was the pinnacle of Soviet planned cities, the culmination of decades of lessons learned from similar projects, and the architectural embodiment of Soviet multinationalism. However, in spite of all the excitement the new city elicited, within just a few years the now-independent Ukrainian government, under pressure from international organizations like the International Atomic Energy Association and the United Nations, established a hard expiration date for the shutdown of all ChNPP reactors, which in turn would mean economic devastation for Slavutych and render its existence wholly unnecessary. Rather than wait for the inevitable, city leaders and residents leveraged global and national interest in the Chornobyl disaster and its aftermath, drawing investments from multinational corporations, international organizations, and states to keep the city alive. Their scattergun approach to economic diversification and pre-emptive urban revitalization paid off, as Slavutych thrived even as the rest of Ukraine suffered major economic and demographic crises. This paper examines how Slavutych citizens were able to forge, and then to act upon, geopolitical relationships to mitigate the lingering social, political, and economic effects of the Chornobyl disaster in the city.
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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 | 0.015 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".