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Unequal Pieces of a Shrinking Pie: The Struggle between African Americans and Latinos over Education, Employment, and Empowerment in Compton, California

2009· article· en· W2153857110 on OpenAlexaboutno aff
Emily E. Straus

Bibliographic record

VenueHistory of Education Quarterly · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceNoticeQuarter (Canadian coin)EmpowermentRhetoricSociologyPolitical scienceGender studiesPsychologyLawHistoryTheology

Abstract

fetched live from OpenAlex

Just days after the start of the 1994-1995 school year, almost a quarter of the student body at McKinley Elementary School in Compton, California did not show up for class. Latino organizers had asked parents to keep their children out of school to protest what they perceived as school administrators' inadequate response to Latino educational needs. Parents of approximately one hundred of the school's 431 students heeded the call. Although one out of four was only slighdy above the daily absentee rate for a normal school day, the nature of these absences forced district administrators to take notice. The rhetoric that Latino activists used when describing the management of Compton's public schools served as the most disquieting aspect of the walkout. “The Compton Unified School District is like Mississippi,” asserted John Ortega, the lead counsel for the Union of Parents and Students of Compton United, the association that organized the attendance strikes. “In Mississippi, they didn't want to educate blacks in the ‘50s, and in the ‘90s, Compton doesn't want to educate Latinos.”

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.570

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0420.019
Scholarly communication0.0090.003
Open science0.0010.011
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.024
GPT teacher head0.309
Teacher spread0.286 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations26
Published2009
Admission routes1
Has abstractyes

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