Narrowing the Gender Gap. Class I Diversity Strategies Help Women Break through the Glass Ceiling
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it