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Meeting Great Expectations: The Experiences of Minority Students at a Canadian University

2017· book-chapter· en· W2606929791 on OpenAlexaboutno aff
Daniyal Zuberi, Melita Ptashnick

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionIdentity (music)Medical educationQualitative researchPopulationQuality (philosophy)PsychologyHigher educationPedagogyPolitical scienceSociologyMedicineSocial science

Abstract

fetched live from OpenAlex

Abstract As Canadian universities increasingly serve diverse student populations, there is a need to understand the experiences of racialized students, including their experiences of bias and perception of the quality of postsecondary education. We utilize qualitative interviews with 38 ‘Asian-Canadian’ undergraduate students at a Canadian university as a case study to explore challenges to identity expression, strategies to earn admission, and campus resources. The findings reveal that students’ perceive stereotyping. They point to their families as preparing them for university admission as well as describing extracurricular endeavours and international baccalaureate education as helping them meet admission requirements. Study participants described challenges in university, including accessing some services. The findings are limited in the sense of not being able to distinguish whether the concerns related to access to resources was unique to these students or the broader student population. More research is needed on the experience of racialized students in Canadian postsecondary institutions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.688
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.372
Teacher spread0.320 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

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