Bibliographic record
Abstract
本案例介绍了施耐德电气过去15年间在多元化、公平性、包容性（DEI）方面的探索。施耐德电气致力于“为所有员工提供平等机会，无论他们是谁或身在何地，确保他们都能受到重视并能放心地为公司献力”。为了推动DEI，施耐德电气多管齐下。员工的态度也从一开始的质疑转变为接受，继而开始思考如何落实。 本案例以施耐德电气首席人力资源官（CHRO）Charise Le面临的艰难抉择结尾。作为一名华裔女高管，Charise对集团的DEI战略和文化心存感激——如若集团不奉行DEI，她便不可能在中国本土承接集团的全球职责。2022年初，她领导的招聘小组需要敲定施耐德电气某国总裁职位的最终人选，而她在John Carney和Lucy Chiang之间摇摆不定。从业务角度来看，两位候选人都无可挑剔。Lucy的专长似乎更符合公司的战略需求，但她在人员管理方面稍显不足。John曾在领导新成立的脱碳倡议时表现出较弱的影响力，但他的包容性更高，受到前下属的一致认可。 这个艰抉抉择让Charise夜不能寐。任命Lucy为施耐德电气某国总裁将推进“多元化”，但这对John公平吗？也就是说，追求“多元化”是否会牺牲“公平性”？Charise和她领导的招聘小组该如何践行集团的DEI原则？
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".