Methodology Focused on Identifying Variables Necessary to Develop Logistics Clusters
Bibliographic record
Abstract
Several scholars have addressed the locational factors necessary for the best installation of industries or services; among them, one finds the costs with transportation of products and raw materials, labor-related costs, benefits deriving from the agglomeration of companies, as well as place-environment associations. Some agglomeration types stand out in this context, each one of them has its specific features, although they share the same goal. The agglomeration of companies is an increasingly frequent trend observed in production centers. Companies belonging to the same production chain remain close to each other in order to reduce costs with product transportation, storage and distribution processes. Consequently, they get to optimize their processes and increase their profits. The proximity between companies belonging to the same branch increases competitiveness between them. In addition, there is significant presence of skilled labor in these regions, a fact that favors logistics operations such as the transportation of inputs needed to enable companies’ production, and cost reduction. Thus, the aim of the present research is to create a methodology capable of identifying the variables necessary to develop a logistics cluster based on concepts such as productive economic agglomerations, by taking into consideration aspects addressed in a survey conducted with key cluster policy-development actors. Moreover, Interpretive Structural Modelling (ISM) was used to create an ontology to help better understanding the association among all variables necessary to structure logistics clusters.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".