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。
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.114 |
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; both teacher heads agree on what is shown here.
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".