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Record W7028996241

“I Had Everything Ready and I Didn’t Submit”: Immigrant Youth Navigating The College-Going Process

2024· other· en· W7028996241 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2024
Typeother
Languageen
FieldArts and Humanities
TopicHistorical, Literary, and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPopulationDiversity (politics)Quarter (Canadian coin)ConflationWork (physics)Variation (astronomy)Language proficiency
DOInot available

Abstract

fetched live from OpenAlex

Nearly 27 percent of California’s population are foreign-born and almost a quarter are undocumented. Yet, research is limited on mechanisms affecting immigrants’ college access. We know little about how informational resources (e.g., counselors, peers) support or constrain the college information seeking pursuits of immigrant youth who arrive to the United States (U.S.) before 18. Also less known is how immigrant youth navigate informational resources according to their needs, such as for DREAMer and DACA youth who are undocumented. One hypothesis suggests that access to higher education is related to different arrival ages and integration experiences. Yet much of prior work tends to conflate immigrant youth with 2ndGen (born in the United States to at least one immigrant parent) or combine this population into one category regardless of arrival age, reflecting dataset limitations that preclude disaggregation by arrival age. This practice contradicts theoretical and empirical work suggesting that older and younger arrivals have varied incorporation experiences, including language proficiency and educational attainment. This likely reflects arrival age differences reflecting how relative to childhood arrivals, older arrivals have less familiarity with the American education system. In relation to pursing the college-going path, this implies that older arrivals have less familiarity with the college and financial aid application process. Immigrant youth with less familiarity may encounter more barriers than younger arriving youth. Moreover, the few existing studies on immigrant youth and college-going may exclude legal status or tend to examine a single immigrant group despite the diversity of immigrants in the U.S. Consequently, some of what we know, more precisely assume, about immigrant youth is potentially inaccurate due to data limitations. This dissertation draws on existing nationally representative data as well as a mixed-method approach, original survey and interviews, to address data and empirical research gaps.In the chapter one, using nationally representative data (HSLS), I examine whether there is an association between immigrant youth residing in immigrant-friendly policy states and college enrollment. I find that net of controls, students residing in immigrant-friendly policy states do not experience an increase in enrollment. In chapter two, drawing on interviews with immigrant youth across three higher education institutions in California (two universities and a community college serving large immigrant populations), I investigate how immigrant youth perceive informational resources, such as counselors and alumni, and what factors motivate their college-information search process. Findings illustrate four themes characterizing immigrant youths’ experiences in navigating the postsecondary path: (1) avoidance of counselors, (2) dual frame influence on postsecondary path choices, (3) the powerful role of villages nudges, and (4) “more approachable” connections with alumni. In chapter three, I draw on original survey data collection to examine descriptively the college-information seeking behaviors of immigrant youth across three higher education institutions. Findings shows that a smaller proportion of older arriving immigrant youth are satisfied with counselors’ guidance on college choice and financial aid. Additionally, a larger share of older arriving immigrant youth prefer to meet with counselors online only in contrast to younger arriving immigrant peers and US-born peers. I conclude with a discussion on the overall findings from this dissertation and suggestions for future directions.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.214
Teacher spread0.194 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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