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Record W347238091 · doi:10.5070/d4111024378

“It’s Not Just a Latino Issue”: Policy Recommendations to Better Support a Racially Diverse Population of Undocumented Students

2015· article· en· W347238091 on OpenAlexaboutno aff
Carlos F Salinas Velasco, Trisha Mazumder, Laura E. Enriquez

Bibliographic record

VenueInterActions UCLA Journal of Education and Information Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRacializationImmigrationQuarter (Canadian coin)Political sciencePopulationSociologyPublic relationsGender studiesRace (biology)GeographyLaw

Abstract

fetched live from OpenAlex

Even though almost a quarter of undocumented immigrants are not of Latina/o origin, most academic research and institutional support policies have heavily emphasized the experiences and needs of Latina/o undocumented students. This report highlights the experiences of non-Latina/o undocumented college students in an effort to provide insight into how educators, organizers, and interested stakeholders can better support the needs of a racially diverse undocumented student population. We find that the racialization of undocumented immigration as a Latina/o issue differentiates the experiences of Latina/o and non-Latina/o undocumented students by creating disparities in their access to material resources and social support. Building upon these findings, we draw specific policy recommendations that will help better support all undocumented students’ access to and persistence in higher education.

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)0.017
metaresearch head score (Gemma)0.037
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0110.006
Scholarly communication0.0130.013
Open science0.0050.011
Research integrity0.0210.016
Insufficient payload (model declined to judge)0.0300.003

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 head0.108
GPT teacher head0.536
Teacher spread0.428 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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Same venueInterActions UCLA Journal of Education and Information StudiesSame topicHigher Education Research StudiesFrench-language works237,207