Xihaner: Building a Sustainable Social Enterprise in China
Bibliographic record
Abstract
This case explores the opportunities and challenges of building a sustainable social enterprise in China. It traces the development of Shenzhen Xihaner Car Wash Center (“Xihaner”) in Shenzhen, China, founded in August 2015 by Mr. Jun Cao, the father of an intellectually disabled child. Cao’s goal was to provide gainful employment and continuing care for people with intellectual disability. From the start, he was determined to build a social enterprise that could generate enough profits to become sustainable over the long term and not a charitable organization that relied on donations. With a carefully designed business model, Xihaner made good progress in its first two years. Their achievements won the enterprise the gold award at the Sixth China Charity Fair on 24 September 2017. Despite these achievements, Xihaner still faced many challenges. Cao wondered: Do we have a sustainable business model for our social enterprise? How can we grow Xihaner further to benefit more intellectually disabled people in China?
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".