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Record W2775798254 · doi:10.1080/15313204.2017.1409675

Addressing academic aspirations, challenges, and barriers of indigenous and immigrant students in a postsecondary education setting

2017· article· en· W2775798254 on OpenAlexaffabout
Janki Shankar, Eugene Ip, Ernest Khalema

Bibliographic record

VenueJournal of Ethnic & Cultural Diversity in Social Work · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsNorQuest CollegeUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsIndigenousImmigrationPsychosocialMulticulturalismSociologyPostsecondary educationFace (sociological concept)Qualitative researchPedagogyHigher educationPolitical scienceMedical educationEconomic growthPsychologyMedicineSocial science

Abstract

fetched live from OpenAlex

Canada ranks high among the Organization for Economic Cooperation and Development (OECD) countries in terms of advanced education with 66% of Canadians having completed some form of postsecondary education. Yet, students from indigenous and immigrant backgrounds face several psychosocial and institutional barriers that hinder their academic progress. The current study used a qualitative approach to examine the experiences and challenges faced by indigenous and immigrant learners who were enrolled in a postsecondary human services program in Western Canada. Findings suggest that despite 40 years of a multicultural approach to education these students continue to experience several barriers to continuing their study programs. The need for radicalizing teaching by using alternate critical decolonizing discourses and pedagogy is discussed.

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)0.004
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 score0.166
Threshold uncertainty score0.331

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.005
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.118
GPT teacher head0.410
Teacher spread0.292 · 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

Citations14
Published2017
Admission routes2
Has abstractyes

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