Monotown futures: toxic waste, civic protest and governance struggles in Stepnogorsk, Kazakhstan
Bibliographic record
Abstract
In early June 2020, a few residents of Stepnogorsk learned of a clandestine, state-initiated plan to import toxic PCBs (polychlorinated biphenols) into the city for storage and eventual disposal by a local firm. Alarmed by later news that an incinerator for that waste, among additional waste from throughout Kazakhstan, would be built in ‘their’ city, grass-roots activists organized through social media, door-to-door petitioning and public demonstrations to halt the operation. This public act of refusal gained much media attention for the former Soviet secret city, well known for its uranium mines and myriad environmental issues associated with decades of extraction and waste storage. Rather than opposing industrial development championed through their pro-growth city leadership outright, demonstrators expressed a moral demand for transparency and civic respect without rejecting industrial life, what I describe as aspirational pragmatism. This form of hopeful accountability is a civic position grounded in historical dependencies on state and industry, shaped by post-Soviet economic decline and ongoing toxic exposure, revealing how residents of marginalized industrial cities negotiate survival, recognition and dignity amid environmental harm.
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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 | 0.002 | 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 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".