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Record W7047945548

Interview with Ray Villanueva

2010· article· en· W7047945548 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - CSUMB (California State University, Monterey Bay) · 2010
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsChinatownQuarter (Canadian coin)State (computer science)Work (physics)Accident (philosophy)Brother
DOInot available

Abstract

fetched live from OpenAlex

Mr. Ray Villanueva begins his story of Chinatown by telling how his mother and aunts came to Salinas to work in the fields. They were from a village in the state of Jalisco, Mexico. His Father, and two uncles, Felix and Ramon, came from the Philippines. They hopped a cargo ship from Hawaii, landed in San Pedro, and came to Salinas to work in the fields. His parents met in 1946 and were married in 1947. Ray was born in 1950. At age three or four Ray becomes aware that he lives in a big house, with a big kitchen and lots of people. It is a labor camp. His father runs two labor camps. Ray starts Lincoln School, in Salinas, at age five. The other kids ask him, “Ray, where do you live?” “In a labor camp,” he says. “What’s a labor camp?” they ask. Ray still knows many of the classmates he went to school with. Many have deep roots in the community: The Gongs, Yees, Meyers, Boslers, Jeffries, and Pias. Ray’s dad liked to gamble at the Streamline pool hall and also at the Rex card room. Ray and his friends went into Chinatown and learned how to shine shoes for a quarter for the GIs from Ft. Ord. Mr. Singley, who later had a shop on Monterey street by Lacey's Auto parts and the United Café, taught him how to do a spit shine. They used pomade or kiwi polish to get a good shine. Ray also sold boiled com for a dime in Chinatown and in the labor camp. After his father would harvest a field, Ramon (the name his father called him), his father and sister would go and gather the corn from those fields. His grandmother boiled it for him, and the money they earned bought their school clothes. Ray remembers many of the businesses in Chinatown: Salinas Valley Market; Golden Dragon; the Shell gas station on Market; Republic Café; Leon’s Night Club; Lloyd, the barber; also, Santos, a Filipino barber; La Revancha; Sinaloa, and the Lotus Inn; and especially Mama’s, the family favorite which served traditional Filipino food. Ray talks about his family each having their favorite dish at Mama’s. A coffee can in the center of the table had spoons and all the serving dishes were different. Ray says he considers Chinatown like Salinas Hollywood. It was exciting with bars and clubs and GIs and restaurants. He says it was like a village. For him it stretched from Sun Street to Bridge to Market Street. There were a lot of labor camps on Sun Street. Ray was baptized at Cristo Rey by Padre Hermosa. He says Salinas then was only fields and cowboys. When industry came in the 60s, Nestles and Smuckers, and when the Main street underpass was built people moved away from Chinatown. Ray’s father bought a house near Front and Market in 1961. When he asked his Father why he had waited so long, he was told that prejudice against Filipinos prevented them from buying houses, and when they could they were only in certain areas. Ray was aware that his parents had faced discrimination. He says he did not feel it at school, at Lincoln and later, but he knows his father did. His father would tell him it was not right to talk to white women when Ray was older and would go with his friends to the theatres on Main Street. He is aware of prejudice toward Filipinos and Asians and has studied about the Salinas lettuce wars of the 40s and 50s. He tells a story of the Sunset Beach Filipino labor camp fire, and a restaurant on South Main that had a sign, “No dogs, or Filipinos.”

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.174
Teacher spread0.167 · 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; a candidate call from one teacher head, not a consensus.

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
Published2010
Admission routes1
Has abstractyes

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