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Record W2910164756 · doi:10.1111/ijsw.12372

Mapping social work education in the West Africa region: Movements toward indigenization in 12 countries’ training programs

2019· article· en· W2910164756 on OpenAlexaff
Mark Canavera, Bree Akesson, Debbie Landis, Miranda Armstrong, Elizabeth Meyer

Bibliographic record

VenueInternational Journal of Social Welfare · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsWilfrid Laurier University
FundersUNICEF
KeywordsIndigenizationSocial workWorkforceIndigenousEconomic growthSocial WelfareWelfareTraining (meteorology)Field researchWork (physics)Political scienceSociologySocioeconomicsGender studiesSocial scienceGeographyAnthropology

Abstract

fetched live from OpenAlex

This article presents the results of a systematic mapping of social work training programs in countries throughout West Africa, a region historically under‐represented in global discussions of the social welfare workforce. The research illuminates how social workers and related professionals are trained to engage in social work practice in a number of West African countries. The research was conducted in two phases. In the initial phase, the research team collected documents from 12 West African countries and conducted phone interviews with relevant individuals. The second phase included field research in five West African countries − Burkina Faso, Côte d’Ivoire, Ghana, Nigeria, and Senegal − where the research team conducted semi‐structured interviews and group discussions with 253 individuals. Framed by indigenization theory, this study describes social service training institutes in West Africa and highlights the varying degrees to which programs have been adapted to indigenous and endogenous realities in the postcolonial era.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.355
Teacher spread0.298 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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