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Record W7132734813

DEI at Schneider Electric: From "Why" to "How"

2023· other· en· W7132734813 on OpenAlex
Siew Kim Jean Lee, Liman Zhao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCEIBS Institutional Repository · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsPosition (finance)Inclusion (mineral)OfficerDiversity (politics)Government (linguistics)Mainland China
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.059

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.

Opus teacher head0.017
GPT teacher head0.244
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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