Leadership Development Programs for Women in Engineering Industry
Bibliographic record
Abstract
Many engineering organizations are realizing the benefits of diversity for innovation in their product development and team dynamics. While women remain a minority in the classroom and the workplace when it comes to engineering, they have registered significant gains. Despite decades of increases in percentages of women at lower levels however, gender diversity at high levels remains woefully low. Without integrating diversity at all levels of an organization, the full benefits of diversity cannot be reached. Furthermore, highly trained and experienced workers become dissatisfied and/or eventually leave if they cannot see a path to career advancement. Leadership development programs for women have recently sprung up in a number of engineering organizations to reap the full benefits of these companies’ investments in a diverse workforce. At Pratt & Whitney Canada, in 2007, a committee was struck to develop a Women’s Leadership Initiative that has been vibrant ever since, registering successes such as promotion of several women to Vice President status. In 2011, the NSERC Chair for Women in Science and Engineering, BC and Yukon sponsored six introductory Leadership Development workshops developed by the Canadian Centre for Women in Science, Engineering, Trades and Technology hosted at engineering workplaces across British Columbia. The presentation and paper will discuss the need for such programs, their essential ingredients and provide a preliminary assessment of their effectiveness.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".