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Record W2909608845

Rethinking Western Muslim Identity with Social Representations

2018· article· en· W2909608845 on OpenAlexaff
Tarek Younis, Ghayda Hassan

Bibliographic record

VenuePapers on Social Representations · 2018
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsIdentity (music)Social identity approachConstruct (python library)Social identity theoryEpistemologyReligious identitySociologyIdentity formationDepictionSocial groupSocial psychologyConsciousnessPoliticsGender studiesPsychologyPolitical scienceSelf-conceptAestheticsLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

The research subject of social identity among Western Muslims raises concern, as it is questionable if one can dissociate its political implications from academic analysis. This article uses the concept of social representations as a viable alternative in providing a more nuanced depiction of Western Muslim identity dynamics. We first illustrate the need to go beyond the identity construct in social psychology, as it may potentially reproduce the moral panic surrounding Muslims in public consciousness. We then propose an alternative conceptualisation Western Muslim identity - using social representations - which emphasizes the importance of common-sensical knowledge structures. We discuss the necessity of understanding Western Muslim group dynamics without politically reifying the implicit incongruity of national/religious affiliations via the construct of ‘identity’.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.033
Scholarly communication0.0070.010
Open science0.0010.009
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 head0.185
GPT teacher head0.512
Teacher spread0.327 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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