“I Had Everything Ready and I Didn’t Submit”: Immigrant Youth Navigating The College-Going Process
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".