Winter Texans: A Study of Community and Belonging in an Overlooked Border Identity in the Lower Rio Grande Valley
Bibliographic record
Abstract
This study centers around a population of retired Anglos who migrate to South Texas border towns in the Lower Rio Grande Valley (locally known as The Valley) from October through March each year. Many travel in recreational vehicles (RVs) and hail from the United States and Canada to set up home in one of The Valley’s RV parks. They are called Winter Texans by themselves and locals. Scholarly research focused on Winter Texans explores how they are motivated to move because of lower costs of living (Murray 2020), how they travel for medical reasons, and their use of Mexican health care as an alternative to high costs in the United States (Dalstrom 2012). A performance analysis of RV parks has been conducted as part of a tourism management study (Sheng 2014) and The University of Texas Rio Grande Valley periodically publishes surveys of Winter Texans as part of their Tourism and Business Management Research Center. However, little research has been done on the everyday experience of Winter Texans, their networks and relationships, or Winter Texans as a border identity, even though the population in The Valley swells by over 100,000 each October through March. The University of Texas Winter Texan Survey of 2018 reported that border towns in Mexico receive an annual economic boost of more than 32 million dollars from Winter Texans, while border towns in The Valley reap an economic impact of over 528 million dollars annually. This thesis is the result of my research with Winter Texans, as I observed them in their daily activities and lived amongst them for three months, discovering who they are and how they find community and belonging on the U.S.-Mexico border. This paper examines an overlooked border identity, opening the door for further research on this topic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".