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Record W4404723287 · doi:10.5296/ijsw.v11i2.22291

Globalization, Colonization, Islamization, and Social Work Practice

2024· article· en· W4404723287 on OpenAlexaff
Alean Al-Krenawi, Julianne Hashmi

Bibliographic record

VenueInternational Journal of Social Work · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsAlgoma University
Fundersnot available
KeywordsGlobalizationIslamizationColonizationWork (physics)Political scienceSociologyIslamGeographyBiologyEngineeringEcology

Abstract

fetched live from OpenAlex

This paper explores the critical intersection of social work and Islam, focusing on the Middle East and North Africa (MENA) region amidst historical and ongoing conflicts. The study examines how Western-oriented social work education often clashes with the realities faced by Muslim populations who interpret the world through an Islamic lens. The impact of colonialism, modernization, and globalization on social work practices in the MENA region is emphasized, highlighting the enduring effects of these forces on local knowledge and the marginalization of indigenous Islamic wisdom. The study advocates for a paradigm shift toward authentization—developing social work approaches grounded in the local cultural and religious contexts of Muslim populations. It discusses the integration of Islamic practices and values into social work and psychotherapy, emphasizing the therapeutic benefits of Islamic rituals, spirituality, and community-oriented practices. The paper also underscores the need for culturally relevant social work education that reflects the diverse realities of Muslim clients. By promoting a more inclusive and culturally sensitive approach, the article aims to bridge the gap between Western social work practices and the needs of Muslim communities, fostering better outcomes and enhanced well-being for those served.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.013
GPT teacher head0.332
Teacher spread0.319 · 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 designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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