The Geographic and Demographic Challenges To the Regional Institutionalization Of the Texas Lower Rio Grande Valley [Los desafíos geográficos y democráticos de la institucionalización regional del Valle Bajo del Río Grande de Texas]
Bibliographic record
Abstract
The purpose of this article is to examine the institutionalization of a region of the United States of America: the Texas Lower Rio Grande Valley. It will use and build on theories of regional institutionalization, geography, and demography by Paasi, Harvey, Gilbert, and other theorists. This research asks how history, geography, and demography challenge the present and future regional institutionalization of the Texas Lower Rio Grande Valley. To answer this question, recent case studies on institutionalization and regional development in Canadian and U.S.–Mexican regions will be used to explain phenomena. - El propósito del presente documento es analizar la institucionalización de una región de Estados Unidos: el Valle Bajo del Río Grande de Texas. Para ello aplicaré teorías sobre institucionalización regional, geografía y demografía de Paasi, Harvey, Gilbert y otros teóricos. En esta investigación indagaré cómo la historia, la geografía y la demografía enfrentan desde hoy y hacia el futuro la institucionalización regional de ese sitio. Para responder este interrogante y explicar el fenómeno se tomarán en cuenta estudios de caso sobre la institucionalización y el desarrollo regional en Canadá y en regiones de influencia conjunta para Estados Unidos y México.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".