The Regional Peculiarities and Identity of the Ural Old Industrial District: Socio-Topological Aspects
Bibliographic record
Abstract
Starting with the large-scale transformations of Peter the Great in the first quarter of the eighteenth century, the mining industry of the Urals was subjected to increased state and legal regulation. Based on theoretical aspects of social topology, an attempt is made to describe the influence of the regional peculiarities and identity of the Ural old industrial region on the forms of management and social practices of the region’s mining economy. The work’s purpose is to identify the difference in the organisation of factory production, depending on the form of ownership of the enterprise: state, possessional, or allodial. It is necessary to identify the role of administrative and economic practices and constraining and developing factors: the vast territory and the predominance of traditional management and everyday practices, on which private and state entrepreneurship had been based on for a long time. The dual system of mining and civil administrations led to the emergence of complex phenomena like the district system (V. V. Adamov) and polymorphism (I. V. Poberezhnikov). The authors try to substantiate one of the main reasons for increased social turbulence in the early twentieth century. On the basis of this study, it can be assumed that in the conditions of a balanced development of industry at the micro-, meso-, and macro- levels, the need to resort to the levers of mobilisation and forced modernisation decreased due to the sufficient availability of financial, industrial, natural, and human resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".