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Record W6987234693

In the Shadows of the Ivory Tower: Undocumented Undergraduates and the Liminal State of Immigration Reform

2015· article· en· W6987234693 on OpenAlexaboutno aff

Bibliographic record

VenueVTechWorks (Virginia Tech) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsLiminalityImmigrationState (computer science)PopulationImmigration reformQuarter (Canadian coin)Sample (material)Private sectorHigher education
DOInot available

Abstract

fetched live from OpenAlex

Amidst the turbulent cross-currents of immigration reform, nearly a quarter of a million undocumented undergraduates are struggling to find their way in higher education. Their liminal state calls for research to inform the unique needs and challenges of this growing student population. In this report, the authors shed light on the range and complexities of undocumented undergraduates experiences based on a sample of 909 participants across 34 states originating in 55 countries. The participants attended an array of postsecondary institutions including two-year and four-year public and private colleges that range in selectivity. In this report, the authors describe their demographic characteristics, experiences in college, as well as their aspirations and anxieties. Further, they make specific recommendations for what colleges should consider to better serve this population. Lastly, in light of executive actions in 2012 and 2014, this data can be used to extrapolate some of the issues that are likely to define this newly protected immigrant population moving forward.

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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.012
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.299
Teacher spread0.284 · 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
Published2015
Admission routes1
Has abstractyes

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