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Record W3210185467 · doi:10.32920/ryerson.14656263.v1

Barriers vs. bridges: undocumented immigrants' access to post-secondary education in the United States and Canada

2021· preprint· en· W3210185467 on OpenAlexaffabout
Marietta Armanyous

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsImmigrationPolitical scienceContext (archaeology)Plan (archaeology)State (computer science)Economic growthPublic administrationGeographyLawEconomics

Abstract

fetched live from OpenAlex

There is limited research available on the issue concerning undocumented immigrants’ access to post-secondary education in the Canadian context. This paper addresses this matter by highlighting the pro-access efforts currently being made in Canada and outlining various actions taken in the United States (US), which Canada can also implement. Guided by the human right to education as its theoretical framework, this paper examines the existing barriers to accessing post-secondary education that this particular marginalized group faces in Canada and the US. It assesses the policies that US state governments, US universities, and Canadian universities implemented (or plan to implement) to remove these barriers, thereby allowing undocumented immigrants to obtain formal post-secondary education. Key Words: undocumented immigrants; post-secondary education; human right to education; Canada; United States

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0140.005
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.331
Teacher spread0.317 · 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
Published2021
Admission routes2
Has abstractyes

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