Bibliographic record
Abstract
This case introduces Schneider Electric’s 15-year-long exploration of Diversity, Equity, and Inclusion (DEI), aimed at providing “equal opportunities to everyone everywhere and to ensure all employees feel uniquely valued and safe to contribute their best.” Through various approaches to initiate DEI, Schneider Electric has seen a shift from employees questioning DEI to accepting it and asking how to implement it. This case ends with a tough decision for Charise Le, Chief Human Resources Officer (CHRO) at Schneider Electric. As a Chinese woman leader, Charise appreciated the Group’s DEI strategy and culture, allowing her to assume a global role in mainland China. Early in 2022, she had to make her own choice and lead the recruiting panel on a final decision between two candidates (John Carney and Lucy Chiang) for a country president position at Schneider Electric. Both John and Lucy were outstanding from a business perspective. Lucy’s expertise seemed to meet the business strategy needs better, but she had issues with managing people. Comparatively, John had a weaker impact on leading newly formed decarbonization initiatives but acted as a more inclusive leader who enjoyed wide acceptance among all his prior subordinates. This decision kept Charise up at night. Appointing Lucy will make the Group’s country presidents more diversified but Charise worries whether it is a “fair play” to John. That is, does focusing on diversity lead to sacrificing “equity” in this case? How should Charise and the recruiting panel practice the Group’s DEI principles in this decision?
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 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; 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".