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Record W4299940200

Identifying Social Service Needs of Muslims Living in a Post 9/11 Era: The Role of Community-Based Organizations

2013· article· en· W4299940200 on OpenAlexaff
John R. Graham

Bibliographic record

VenueTSpace · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsService (business)Social workBusinessPublic relationsSociologyPolitical scienceMarketingLaw
DOInot available

Abstract

fetched live from OpenAlex

In this qualitative study the investigators sought to better understand the ways in which service provider organizations (n=19) working with Muslim service providers have adapted to the changing social and political contexts in a post-9/11 era in New York City, and how this changing environment has affected the types of services that Muslims need. Service providers described two general ways in which services were adapted: 1) they have sought to address limits in service delivery programs that were a result of emerging sociopolitical dynamics (such as increasing discrimination) through adaptations to existing programs or through the development of new initiatives, programs, and organizations; and 2) they have adapted programs and services to meet the emerging sociocultural demands (such as changing attitudes towards help-seeking, and presenting problems of services users) of the Muslim population. The study illustrated the role of service provider organizations in adapting existing services, or creating new services, in response to a changing sociopolitical context. Social work education must focus attention on how social workers can adapt and create organizations that are responsive to the changing needs of service users. More curriculum content is necessary on the intra- and inter-organizational context of direct social work practice, with particular attention to innovation and adaptation within and between human service organizations.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.349
Teacher spread0.314 · 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 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

Citations4
Published2013
Admission routes1
Has abstractyes

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