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Record W7060687894

Interview no. 1684

2016· article· en· W7060687894 on OpenAlexaboutno aff

Bibliographic record

Venuescholarworks - UTEP (The University of Texas at El Paso) · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipMinor (academic)Work (physics)PoliticsImmigrationQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Senator José Rodríguez was born in Alice, Texas and grew up in Alamo, Texas in the Lower Rio Grande Valley. He went to grade school in the mid-fifties when Mexican-American students went to separate schools from Anglo students. These schools did not have all of the resources that were in the Anglo schools. If they spoke Spanish they were paddled. He felt like he did not receive a very challenging elementary education. In middle and high school he came across teachers that saw potential in him and encouraged him to read books. They would give him books to take when his family would leave to work in the fields in the migrant streams. The area was a predominantly agricultural region and all the families he knew were migrant farm workers. It was common for the kids to work after school and weekends while in Alamo. From late April and May they would leave to go work in the Midwest and West Coast and come back sometimes as late as October. He was always trying to catch up in school which made it challenging and difficult to get involved in extracurricular activities. He believes that books were a big factor for him to be able to obtain his education. He developed an interest to go to college even though it was expected for migrant farm workers his age to drop out. He studied political science with a minor in English and speech at the Pan American University on a migrant scholarship from the Minnesota Sugar Company. He went to law school at George Washington University in D.C where he advocated for issues regarding Mexican-American students such as low resources, high dropout rates, and representation in school boards. After law school, he worked in the Department of Housing and Urban Development. A few years later, around the time his father’s health started to deteriorate, his friend invited him to work in the Texas Rural Legal Aid for farm workers in South Texas and went back home. Eventually he became the statewide director of the program. He met his wife Carmen from El Paso and they opened up a Texas Rural Legal Aid farm worker office there. He worked ten years in legal services. They had clients from Juárez and all over the Trans-Pecos area. He now works in the Senate Education Committee. He feels that there has been a lot of progress regarding Mexican- American students in Texas but issues still persist. The dropout rate is almost as high as it was before, over 50% in some areas. He states that the Texas Educational System has been determined to inadequately fund these schools especially programs for English Language Learners and Bilingual Education. The achievement gap between Latino students and others is huge. He believes there is a lot of work to do to offer an opportunity to all of their students. He states that if there is no investment in their education and preparation, then Texas will not be competitive in the global economy. He thinks that is important to support efforts at the local level as well as support STEM (science, technology, engineering, and math) areas because that is where the global economy is at and headed to. His message to young people in the community is to get their education to the fullest extent that they can. If they want to be successful and have a high quality life, they need a solid education whether it is college or a trade school. He encourages them to use their talents to change things, improve peoples’ lives, and to eliminate barriers. He believes this is the most rewarding aspect in life above material wealth.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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
Published2016
Admission routes1
Has abstractyes

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