Bibliographic record
Abstract
本案例讲述了成立于21世纪初的海云地产公司在中国房地产行业进入“快周转”时代以后，为实现提高效率的战略目标而进行绩效管理制度改革的经历。2018年12月，创始人兼CEO凌云发现公司的重点项目进度滞后，但同时几乎所有员工都完成了自己的绩效目标，这让凌云意识到绩效管理存在问题。在凌云的授意下，人力资源总监刘敏开始改革绩效管理制度。新方案针对中基层绩效考核结果与薪酬不挂钩、绩效考核标准与公司战略不挂钩，高层年度考核不利于管理措施调整，不同职能条线之间难以协同等问题做了调整。对于中基层员工，增加由团队绩效、个人绩效共同决定的绩效工资部分；个人绩效目标在主要工作之外，增加了业绩指标和关键节点两个维度，通过自上而下的方式获得；高层员工的考核频率调整为季度考核，并增加360度考核的结果作为晋升的参考；不同部门条线之间则共同承担关键节点责任。新方案实施后，海云地产的业绩有明显提升，但同时又出现了中基层员工轮流绩效垫底等新的问题。绩效管理制度的完善任重道远。
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".