Narrowing the Gender Gap. Class I Diversity Strategies Help Women Break through the Glass Ceiling
Bibliographic record
Abstract
This article describes how, when it comes to closing the gender gap among top leadership posts in corporate America, women made no significant gains in 2011. In fact, women are no higher up the corporate ladder than they were six years ago. The article shows how women remain under-represented at all levels of the workforce of the transportation industry. At U.S. transportation and warehousing companies, women make up 23.1 percent of the industry's labor force, 12.9 percent of executive officers, 13 percent of board directors and 0 percent of chief executive officers. In Canada, the numbers are about the same: women represent 23.6 percent of the labor force, 16.2 percent of senior officers, 14.4 percent of board directors and 0 percent of chief executive officers (CEOs). The numbers come as no surprise to the top official of the international CEO of Women's Transportation Seminar (WTS). Women are under-represented on all steps of the transportation career ladder, but especially at the upper levels, which is why WTS exists — to help women break through the glass ceiling in the transportation industry, including rail. The focus is to narrow that gap and get more women in those executive positions. While Class I executives acknowledge railroads have a way to go to achieve a gender equity in the rail workforce, these executives say that their companies have been successful at narrowing the gender gap through diversity initiatives dedicated to recruiting, retaining and promoting women into positions of authority. The executives say that they want to increase the number of women not just because it's the right thing to do, but because gender diversity makes good business sense. On average, companies with the most women board directors and corporate officers achieve better financial results than companies with few or no women in leadership posts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".