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Record W4251808383 · doi:10.32920/ryerson.14656605

Reconstructing the narratives around young Muslim men in the GTA

2021· preprint· en· W4251808383 on OpenAlexaffabout
Huda Hussain

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsNarrativeOrientalismGender studiesPerceptionIslamophobiaResistance (ecology)PsychologyRacismQualitative researchSocial psychologySociologyIslamHistoryLiteratureSocial scienceArt

Abstract

fetched live from OpenAlex

This research explores the narratives of young Muslim men and challenges social work practitioners to be more critical about their own implicit biases towards them. Existing literature on young Muslim men have not focused on the positive narratives that I personally and professionally know of them. This research examined the harmful impact the historical and current master narrative has on young Muslim men. This was a qualitative study examining Canadian Muslim men. Participants were interviewed through open-ended questions to examine how they continued to thrive regardless of the problematic notions that surround them, the strategies they used to navigate to be successful and their self-perception. The findings present a compelling case for rethinking about the way young Muslim men are perceived, using orientalism in reconstructing how we perceive them. In conclusion, although themes were common amongst both participants, the experiences of young Muslim men cannot be generalized. Key words: Young Muslim men, anti-Muslim racism, orientalism, narrative resistance, social work

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)0.006
metaresearch head score (Gemma)0.006
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 score0.940
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0150.019
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0020.003
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 head0.030
GPT teacher head0.231
Teacher spread0.202 · 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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