Bibliographic record
Abstract
本案例讲述了一名管理培训生李芬在智能手机生产厂商A公司工作时的经历。2008年，李芬刚进入正式岗位便被委以重任，先后承担了一个拓荒的新项目和一个善后的重大项目的人资行政工作。李芬是个自律且对工作有着高标准的人，在她的带领下，两个项目都进展顺利。但李芬发现她很难与同事和睦相处，无论是上级、平级还是下属，都对她颇有微词甚至爆发冲突。李芬从未想到人际关系会成为她发展的障碍。心灰意冷的她决定回总部学习以强化自己的业务能力。但业务能力提高能让她的人际关系问题迎刃而解吗？总被人说情商低的她想不明白：难道工作不是把事情做好就可以了吗？做好工作跟情商高低有什么关系呢？管理者为什么还需要在工作时处理自己与他人的情绪？为什么要对他人抱有同理心呢？怎么才能提高自己的情商和人际关系呢？
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".