Designing and Creating Global Economic/Business Regions: A Case Study of the Rio Grande Valley Region
Bibliographic record
Abstract
This study focuses on the present, and the possible future, economic dynamics in the Lower Rio Grande Valley region. The main goal of the study is to identify factors that can be used to transform the region into a major global economic region. Since the Lower Rio Grande Valley region is not yet a major global economic region, this study also analyzes how to design and implement the frameworks of factors that will transform the region into such a major global economic region. A new conceptual framework, termed premier global economic/business region conceptual or theoretical framework, is created by this study to guide and gird this study. The Oresund region (Denmark–Sweden) and the Puget Sound region (United States–Canada) are models of what Lower Rio Grande Valley can be transformed into in the future. Therefore, this study draws from the main features of the Oresund region and the Puget Sound region to create the premier global economic/business region conceptual or theoretical framework. Essentially, this theoretical framework consists of 12 factors or variables that can be used to transform an international region into a premier global economic/business region. A significant portion of this study analyzes how each of these factors or variables can be designed and utilized to help transform the Lower Rio Grande Valley region into premier global economic/business region.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".