The JinShe community financial education project: a case study of localised financial social work practice in China
Bibliographic record
Abstract
The JinShe Community Financial Education Project launched in 2021. Grounded in the Financial Capability and Asset Building (FCAB) framework, it addresses financial capability gaps among vulnerable populations through a “dual-track empowerment” approach targeting access, knowledge, skills, attitudes, and behaviours. The project established a rigorous “training + supervision + support” financial social worker cultivation system. Service delivery operates through embedded stations employing diverse methods.As of April 2025, the project expanded to 84 communities across 29 cities. It trained 164 social workers and over 550 volunteers, delivering 950+ activities to 200,000+ residents. Evaluation results show significant improvements: financial capability scores increased by 14.2%, fraud identification accuracy rose from 56.8% to 75.4%, appropriate investment selection increased from 58.7% to 74.8%, and credit card overdue rates declined from 9.1% to 4.0%. The JinShe Project offers a robust model and contribute to a “distinctively Chinese approach” with global applicability.金社工程——社区金融教育项目设立于2021年，旨在探索金融教育本土化模式。该项目以金融能力与资产建设为理论指导，采用“双轨赋能”路径，从机会获取、知识普及、技能培养、态度塑造和行为改变五个维度入手提升社区弱势群体金融能力。项目构建起金融教育多方协作模式，对项目社工建立”培训+督导+支持”培养体系，建立社区嵌入式金融教育服务站，广泛采用情景模拟、角色扮演、金融桌游等互动式教学方法。截至2025年4月，项目已拓展至29个城市84社区，培养164名社工和550余名志愿者，开展950余场活动，惠及20万居民。评估显示，参与居民的金融能力得分提升14.2%，诈骗识别率从56.8%升至75.4%，投资适当率从58.7%增至74.8%，信用卡逾期率从9.1%降至4.0%。金社工程将金融教育建构为嵌入社区日常生活的社会性实践，形成了具有鲜明中国特色且具备全球适用性的金融社会工作实践路径。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".