Territorial – Means a Spatial, or a New Approach to the Old Criminological Problem Solving
Bibliographic record
Abstract
The article presents a new approach to the possibilities of using the method of analysis of patterns of territorial differences formation in crime rates in regions of the same country in order to expand the understanding of crime causative complex as a whole. The author proves that the effectiveness of territorial approach to crime causes understanding will reach its greatest extent when it is a criminological analysis of territorial-spacial systems functioning, a revelation of their destabilizing factors, sources of disorganization and social tension. But this requires a preliminary solution to the problem of identifying the patterns of territorial socio-economic systems functioning, that the author proposes to do on the basis of the "New Economic Geography" modern achievements. For such an approach, the name "spatial analysis of causative crime complex" is claimed. This analysis, carried with reference to some regions of Central Federal District of Russia, demonstrated the received results importance for a better mechanism of theoretical understanding of crime existence in society. In particular, the conclusion was made that the main reason of crime rates increasing within the territorial socio-economic system (specifically, the Russian Federation region) is the imbalance between theoretically estimated potential of socio-economic development of a definite region at a certain stage of society development and actual vector and dynamics of such development in reality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".