Bibliographic record
Abstract
本案例由案例正文和补充材料两个部分构成，聚焦工作动机对个体行为和工作设计的影响这一问题。其中案例正文讲述了38岁的台湾房产中介公司A公司资深员工罗以伦从总部调任杭州分公司的过程，并对调任前后公司及市场的情况、主人公的所思所想进行了详细地叙述，展现了个人职业发展、薪酬、家庭、价值观等因素对行为的影响。补充材料则提供了两种截然不同的故事背景，借以说明重要他人的不同态度对华人动机模式的影响。本案例故事精炼，同时包罗了影响行为的主要因素，并展现了动机动态变化的过程，能够帮助学生较为深刻地理解自我决定理论，并为工作设计等管理实务提供借鉴。
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.032 |
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".