MétaCan
Menu
Back to cohort
Record W1796784500 · doi:10.47678/cjhe.v32i2.183409

Meeting Immigrant Community College Students' Needs on One Greater Toronto Area College Campus

2002· article· en· W1796784500 on OpenAlexaffvenueabout
Kenise Murphy Kilbride, Lucy D’Arcangelo

Bibliographic record

VenueCanadian Journal of Higher Education · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsToronto Metropolitan UniversityGeorge Brown College
Fundersnot available
KeywordsCommunity collegeImmigrationHigher educationMedical educationSociologyPsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

One hundred and forty-six students who entered Canada after their twelfth birthday and are now in one of six technical programs on a Greater Toronto Area (GTA) community college campus were surveyed. Technical programs enrol over half the students on this campus, and the six programs enrol over half the technical students. Over half had entered Canada past the usual age for high school (and over two-thirds in the past six years), making the college their point of entry into the Canadian educational system. Degrees and types of needs were analyzed, as well as degrees and sources of support. Differences occurred across numerous background traits but the most striking finding is the students' perception of a low degree of support from the college itself. This has clear policy implications for funders of GTA colleges, which attract such high numbers of new immigrants to Canada.

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)0.003
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 score0.324
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.039
GPT teacher head0.292
Teacher spread0.254 · 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

Citations19
Published2002
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicMigration, Ethnicity, and EconomyFrench-language works237,207