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Record W7066140635

How The Railroad Help Built Texas (Texas Was Built By Railroads)

2022· article· en· W7066140635 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsMileGovernment (linguistics)State (computer science)Quarter (Canadian coin)Track (disk drive)LoanConstitutionCensus
DOInot available

Abstract

fetched live from OpenAlex

Early Texas towns took hold alongside protected bays: think Galveston and Corpus Christi. Others developed along the banks of fine rivers, such as San Antonio, Goliad and El Paso. But later, it was the steel tributaries called railroads that planted the seeds that raised towns alongside them. Railroads, more than any other technology, ushered Texas into the industrial age and commercial wealth. The railroads got a slow start in Texas, but the 1876 constitution changed that. It gave away 16 sections of land for every mile of railroad constructed. And tracks began to get laid down with urgency. Imagine, for every mile of track you constructed, your company would get over 10,000 acres as payment! And that wasn’t all. You’d also get a government loan of up to $6,000 dollars per mile to help you pay for the building of that railroad. Within a few years, the state had to cancel the program. Texas was running out of land. It gave away 32 million acres under this and similar programs. That’s 20 percent of all of Texas! And I hate to tell you, there was some corruption involved, too. There were many reasons that townships took hold along railroads. One was because every twenty miles or so, steam locomotives needed water. So water depots were built, even if the place wasn’t otherwise so favorable a spot for a town to pop up. Valentine, Texas, is such a case. The story goes that the stop for the water depot was given that name because the railroad crew reached it on Valentine’s Day, 1882, although another story says it was named for John Valentine: a big stockholder in the railroad. Both could certainly be true – a twofer, you might say. The little town of 130 or so people still exists, and does a booming business at the post office on Valentine’s Day. Other towns sprang up haphazardly because of grading camps lasting long enough for communal roots to grow. Langtry, Texas, was such a place: a tent town where railroad bed builders lived for some time. Langtry was named for George Langtry, a railroad engineer for the Southern Pacific. But Judge Roy Bean, famous as the law West of the Pecos, rewrote that history by force of personality. He sold worldwide the fiction that he had named the town for Lillie Langtry, the gorgeous British-American actress. He was so successful in selling his myth that she stopped to visit her namesake town – and Bean’s grave, shortly after he died. Abilene, Texas was created by the building of the Texas and Pacific Railway line, cattlemen and developers working in concert. They wanted a cattle market similar to Abilene, Kansas – the city for which Abilene, Texas was named. They needed a place where such a market might develop. The railroad bypassed the better-established community of Buffalo Gap. Many of its citizens soon moved to Abilene, since the railroad lines were the life-blood of commerce in those days. Alongside those tracks on the first day of lot selling, the First Presbyterian Church was founded. It prophesied the future of Abilene, which has the reputation of more churches per population than any town in Texas, along with three Christian colleges. In 1860, the Houston and Texas Central Railways built a line through the region that would become College Station – so-named largely because the railroad was there. Texas A&M University, originally The Agricultural and Mechanical College of Texas, opened its doors in 1876. The next year, the U.S. Postal Service designated the town College Station, since that was the name of the railroad passenger depot at the new college. The railroad is still there, bifurcating the enormous 5,200 acre campus that it once bordered only on the west side. Texas has more miles of railroad than any state in the union: 10,539 miles of them, still powering our state’s commercial prowess.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0080.004
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.231
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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