Territorial marketing and its role in determining regional competitiveness. Evaluating supply chain management
Bibliographic record
Abstract
Nowadays, development and sustainability are often combined in the analysis of regional and local processes.In this case, the definition of both competitiveness and sustainability of development require adequate interpretation and quantitative assessment.Territorial marketing is used as a tool to assess the competitiveness of a region.The main purpose of our research is to analyze the methodological and practical aspects of the sustainable development strategy of competitiveness of the Kazakhstan regions and the ways to implement it based on territorial marketing.Among the crucial indicators of territorial marketing, which this article tackles, supply chain management draws particular interest.Each indicator includes a set of criteria that best describe it.This is a 10-point rating system, where the region that showed the best result gets 10 points.It is assumed that based on the generally accepted methods the overall competitiveness can be measured, considering the competitiveness of the 5 mentioned indicators, as well as their assessment with regard to the competitiveness of their criteria.The research results showed that the aggregate indicator for all the regions is below average.The findings show that the Turkestan and Pavlodar regions are the most competitive in supply chain management, having the largest number of shipments.The overwhelming majority of Kazakhstan enterprises are small enterprises, which suggests that the logistics services market is still developing.The use of modern information technologies will optimize warehouse operations.A positive result is ensured by effective local regulation since doing business in Kazakhstan is relatively cheap.In our research, we offer some recommendations for improving the territorial indicators that determine the competitiveness of regions..
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".