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Record W6985777701

Simmons, Victoria

2015· article· en· W6985777701 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalResearch@Fordham (Fordham University) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsDanceStyle (visual arts)Latin AmericansClass (philosophy)GirlDiversity (politics)
DOInot available

Abstract

fetched live from OpenAlex

Victoria Simmons-Good grew up in the Patterson Houses. Her parents moved to the Bronx from Harlem for the affordable housing options offered in the Bronx. He earliest memory is from attending PS 18, which was located near her building. On her way to school, she and her friends would always stop at a Candy Store and eat sweets for breakfast. She also remembers attending a day camp during the summer with fellow children living in the Patterson Houses. She also remembers the music her parents listened to, which was mostly Motown and doo-wop.\nShe grew up in the Patterson houses in the late 1950’s and early 1960’s. She remembers it as a very safe environment, with lots of adult supervision. Her parents were very strict with her, beginning when she was in Junior High School. The Patterson Houses were also a culturally divers project, consisting of both African Americans and Puerto Ricans. With all the cultural diversity in her building, she was exposed to Latin music and calls herself a “buddarican,” which is an African American person who speaks Spanish and can dance to Latin Music.\nHer teachers at PS 18 were very strict. She believes that they really wanted their students to live up to their potential, which meant enforcing strict discipline and adhering to the lesson plan. However, the teachers were still very patient. This strict teaching style continued into Junior High, where she was reprimanded for coming to class late from lunch on a regular basis. She also remembers the social clubs, which she says was a fancy name for gang. She was part of a social club called the Socialettes. They would go around the city and do different activities in each of the boroughs. They would also go to dances and organize them in the Patterson houses, where she met her current husband when she was in high school. She also participated in the PAL track team operated by the 40th precinct. At this time PAL had a lot of sports programs for girls, ranging from track and field to Basketball. She and her friends tried to incorporate basketball to their high school gym curriculum, but it was unsuccessful.\nHer current husband is a Vietnam veteran, which moves the conversation to a discussion of the way the draft and the Vietnam War in general affected residents in the Bronx. Many felt that they should not fight for a country that at that time still discriminated against African Americans and therefore talked about moving to Canada. She had lived in the Bronx her entire life, and did not know the discrimination present in the south until she attended Tennessee State University.\nShe remembers heroin coming into the Bronx as early as the middle of the 1960s. Her neighbor was one of the first people she came into contact with that was “strung out.” This was happening while she was in high school. It was about this time, she remembers, when the communal and pleasant feeling of the projects started to disappear. She began to feel afraid to be in her apartment because of the break-ins she was hearing about. She felt that at the time, the crack epidemic was hitting the Black community very hard. After her mother was mugged, the family moved to the west Bronx.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.452

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.6830.364

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 head0.051
GPT teacher head0.297
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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