Sodexo seeks to get more women into 300 top jobs
Bibliographic record
Abstract
Purpose This paper aims to describe how international food and facilities‐management firm Sodexo is trying to increase the number of women in its top jobs, and why the company particularly welcomes job applications from former members of the military. Design/methodology/approach The paper explains the background to these policies and the successes they have achieved. Findings The paper highlights the company's ambition to ensure that a quarter of its 300 top jobs are filled by women by 2015. It details the work of the Sodexo Women's International Forum for Talent (Swift), which includes 20 of the company's senior female executives, representing 12 nationalities, who propose specific actions needed to help women to achieve top‐management jobs. Practical implications The paper explains that the company believes that a military background translates into successful jobs for veterans with Sodexo because they: recognize the importance of teamwork and employ it every day; are accustomed to advancement through their own achievements; and thrive on new assignments and the challenges of new locations. Social implications The paper highlights the benefits of making best use of the talents of women and veterans. Originality/value The paper describes award‐winning initiatives designed to get more women into top jobs and to employ the talents of guardsmen and veterans.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".