Most, the Town that Moved: Coal, Communists and the 'Gypsy Question' in Post-War Czechoslovakia
Bibliographic record
Abstract
Abstract As Czechoslovakia's communist planners continually increased norms for power and coal production in the 1950s through 1970s, the sprawling surface mines of the north Bohemian brown coal basin expanded voraciously, swallowing 116 villages and parts of several larger cities by 1980. Infamously, the entire historic centre of Most was obliterated in order to expose over 85 million tons of coal. Planners envisioned a new city of Most as a model of socialist modernity. Deriding Most's old town as a decaying capitalist relic, officials lauded New Most's spacious and efficient prefabricated high-rises. Adding to the contrast, the majority of Old Most's remaining inhabitants by 1970 were Roma (Gypsies). For communists, the Roma evoked an old order of segregation, class oppression and bad hygiene. By relocating Roma to modern housing, they could 'liquidate once and for all the Gypsy problem'. This article examines the rhetorics of modernity employed as communists sought to 'solve' intertwined coal, gypsy and housing 'problems' in the city of Most. At the crossroads of several related modernising projects in the twentieth century, Most provides insight into connections between ethnic cleansing, social and environmental engineering and urban planning.
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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 | 0.001 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".