Bibliographic record
Abstract
本案例从“开端”“发展”“升级”三个阶段讲述了博世（中国）投资有限公司（简称“博世中国”）打造“教练文化”的背景、挑战、思考和应对策略等，并站在教练俱乐部成立五周年这个时点（2021年11月15日），探索未来如何让“教练文化”发挥更大的价值。 2016年中，为了助力“从一家传统制造型企业向以物联网为中心的科技公司”的战略转型及其所需人才的发展，博世中国正式推出了“教练型领导实践课程”，却在第一天就遭到高管的质疑。通过积极与外部教练配合、调动更多业务高管积极性、运营教练俱乐部等办法，博世中国让越来越多的博世人接受了“教练”理念。然而，在后续发展过程中，面对行业变局和疫情的影响，博世集团自2020年起减少了在华投资。在培训预算大幅降低的情况下，博世中国通过内化教练课、引入团队教练等方式让教练领导力的影响辐射到更多中层管理者和事业部。随后，“教练文化”渐渐形成，但期间企业又不得不做出多项权衡：“做本土文化vs.与全球文化联结”“内部独立认证vs.与ICF认证挂钩”和“主动推动vs.等待被需要”。 经过五年多的探索，博世中国打造“教练文化”的实践取得了一定的成效：于内，得到以智能制造解决方案事业部副总裁韩冬丽为代表的高管的认可，并助力其所在的事业部实现业务发展；于外，获得拉姆(查兰管理实践奖和国际教练协会德国棱镜奖等认可。但是，企业内部对推行“教练文化”的质疑声一直没有间断过，内部不乏业务高管认为，“教练文化”只是锦上添花，在当下企业面临巨大的业务压力时，企业更应该关注人才的技能培训和发展，而不是追求“教练文化”，后者对业务发展没有实际帮助。 2021年11月15日，负责人力资源建设及管理工作的执行副总裁李晓虹女士接到德国总部的邀请，总结博世中国打造“教练文化”的经验，并在更广范围内传播。动力和阻力并存，李晓虹思考着：她应该如何输出博世中国打造“教练文化”的经验呢？相关经验可否在总部或其他组织复制？有哪些适用条件？又该如何推进呢？
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 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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".