Analyzing the production-distribution-consumption cycle using hierarchical modeling methods
Bibliographic record
Abstract
The production-distribution-consumption cycle is one of the main cycles on which the economy state depends on. This study aims to determine the relationship between production, distribution, and consumption of goods within the central place hierarchies using hierarchical modeling (HLM). It allows us to analyze indicators within several levels of data aggregation. The analysis is carried out in the context of 2319 municipalities that are part of 84 regions of the Russian Federation, in 8 federal districts. The results show that hierarchical analysis methods can be used in the productiondistribution-consumption cycle study. As part of the model's results, it was noted that the income of the population and exports, which determine the demands for goods, have a positive impact on the production and sales of goods. At the same time, the relationship between production and wholesale trade, which characterizes the distribution of goods, is not so clear. The production-distributionconsumption cycle study considers the hierarchy of central places, which takes into account the division of the territory into zones based on the functions performed. The methods of hierarchical analysis made it possible to evaluate the effects generated at each level. We managed to take into account the spatial heterogeneity and hierarchical structure of the data describing the productiondistribution-consumption cycle. This will improve the quality of decisions when determining the manufacturing locations, as well as providing a better approach to the development of territories by state authorities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".