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Record W6922168667 · doi:10.11575/prism/48989

Challenges of Inclusion of Muslim Female Students in Social Work Programs in Western Canada

2021· other· en· W6922168667 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2021
Typeother
Languageen
FieldComputer Science
TopicEducational Robotics and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)MulticulturalismSocial workQualitative researchExploratory researchIslamDiversity (politics)Cultural diversityEquity (law)

Abstract

fetched live from OpenAlex

Introduction: Canada is known globally as a multicultural country, yet multiple studies have indicated that Muslim Canadians believe discrimination, based on their religious beliefs, is common. What is less well-understood is the experience of discrimination of Muslim students in post-secondary settings, particularly in social work. Research Questions: What are the experiences of Muslim female students with regards to discrimination inclusion/exclusion, social justice, and equity in social work programs in Western Canada? How do the experiences of Muslim female students in social work programs in Western Canada impact their lives? Methods: The study used a qualitative descriptive phenomenological design. Twelve Muslim female students currently enrolled in or recent graduates (within the past two years) of university social work programs in Western Canada were recruited using convenience sampling methods. Semi-structured exploratory interviews were conducted between September and November 2020 and the phenomenological approach was used for data analysis. Results: Study participants identified experiences of exclusion, stereotyping, and discrimination. A number of challenges, such as the lack of diversity, lack of awareness by professors and students were faced by Muslim female students. These findings could better inform educators and policymakers in formulating culturally appropriate policies to increase diversity and inclusion and to promote intercultural education in Canadian Universities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.957

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.331
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes1
Has abstractyes

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