Rural Systematization as an Instrument of Political Control of the Communist Regime in Romania
Bibliographic record
Abstract
This paper deals with one of the means of communist control over society in Romania: rural systematization. After the Second World War, the Romanian villages underwent radical changes. The main objective of the communist regime was to reduce the number of villages from 13,129 to 10,000 by the year 2000. To this end, feasibility studies were conducted and the villages were classified as viable and non-viable. About a quarter of Romanian's village were threatened. They were classified according to the following criteria: functionality, infrastructure and social and cultural facilities. The community itself, with its traditional and historical values and the role of the private investors were completely ignored. Some of the villages were to be turned into agro-industrial towns, while others were to be abandoned. The priority of the rural strategy was the shaping of the "New Man" who had to be provided with decent living and safety conditions. The result of the territorial systematization process would be the "New Towns", which had to meet the 'New Man's" needs. This required, among other things, new buildings erected after typical design. The purpose of the communist authorities was to homogenize all the members of the society, so that they were easier to control. Another communist priority was to control the migration from the rural to the urban area by optimizing the commuting system. Encouraging population growth and improving the living conditions were the means by which the communist authorities planned to incorporate the rural environment into the urban one.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".