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Record W2377680080

Social Work Capacity Building for Women's Federation staff:A Project-basedCase Study

2011· article· en· W2377680080 on OpenAlexaboutno aff
HU Yanhong

Bibliographic record

VenueJournal of Social Work · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsProfessionalizationWork (physics)Social workCapacity buildingChinaAgency (philosophy)Professional developmentPolitical sciencePublic relationsMedical educationEconomic growthSociologyMedicineEngineeringSocial science
DOInot available

Abstract

fetched live from OpenAlex

Funded by Canadian International Development Agency,China Women's University,in collaboration with the Faculty of Social Work,University of Manitoba,started to implement the Project of Capacity Building for Women's Federation Staff in Rural China.The project was located in Inner Mongolia,Shandong and Sichuan provinces,focusing on providing social work professional training to Women's Federation staff,in an effort to equip the staff with professional social work knowledge,to increase their capacity of service provision,and better meet the needs of rural women,consequently to improve the quality of life for the rural women.The paper will concentrate on evaluating the outcomes of training programs designed for women's federation staff,sharing experiences and lessons learned from the training,and provide implications for future professionalization of social work in China.

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)0.017
metaresearch head score (Gemma)0.011
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 score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0250.005
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.127
GPT teacher head0.397
Teacher spread0.270 · 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
Published2011
Admission routes1
Has abstractyes

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