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Record W1964246069 · doi:10.1215/00382876-1162615

UCLA's Underground Students Rise to Fight for Public Education

2011· article· en· W1964246069 on OpenAlexaboutno aff
Carlos Álvarez Amador

Bibliographic record

VenueSouth Atlantic Quarterly · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsPrivilege (computing)Quarter (Canadian coin)Political scienceState (computer science)Face (sociological concept)Work (physics)Public administrationPublic relationsSociologyLawEngineeringHistorySocial science

Abstract

fetched live from OpenAlex

This essay provides an account of the events that took place during the November 2009 protests against the University of California Regents' tuition hikes at UCLA. Although the tuition hikes affected every student in the UC system, they are particularly difficult for undocumented students, who face such barriers as inaccessibility to most types of financial aid and work-study programs and the inability to secure jobs. The essay describes how hundreds of students from all over California gathered at UCLA to demand equality in the educational system. The essay then elaborates on how race and privilege played out during the planning and execution of the actions. Finally, the author points to the impact that these tuition hikes had on undocumented students in the UC system. Whereas UC president Mark Yudof promised to expand financial aid programs for students of families with an income less than $70,000, undocumented college students could not benefit from these proposals due to restrictions in state laws. To the contrary, one out of five undocumented students dropped out of UCLA in the quarter following the implementation of the tuition hikes. Despite the direct impacts on their accessibility to education, undocumented students continued to organize and expose their plight on the UCLA campus long after the rest of the student body had accepted the hikes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.547

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.312
Teacher spread0.273 · 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.

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

Citations2
Published2011
Admission routes1
Has abstractyes

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