Interview and Focus Group Summary Report
Bibliographic record
Abstract
The representation and experiences of women in Engineering and Information and Computer Technologies (EICT) fields have been a subject of extensive research and discussion. This report provides a summary of the experiences of women in these fields across Canada, Sweden, and Germany. By examining 58 semi-structured interviews and 4 focus groups, this report identifies commonalities and differences in their experiences, challenges, and opportunities. This report sheds light onto factors that influence women's participation and retention in Engineering and ICT and provides recommendations for improving gender equity in these fields. We find that the experiences of women in ICT and Engineering fields in Canada, Sweden, and Germany reveal challenges and differences that align with Esping-Andersen's (1989) Welfare State typology. Sweden's social democratic welfare state offers extensive support for work-life balance and gender equality, reflected in the positive experiences of women in ICT and Engineering. Canada's liberal welfare state provides some support but lacks the comprehensive policies seen in Sweden, leading to challenges in balancing work and family responsibilities. Germany's conservative welfare state shows more traditional gender roles and less flexibility in work arrangements, contributing to the greater challenges faced by women in these fields. Addressing gender bias, improving work-life balance, and providing better access to mentorship and career development opportunities are crucial steps in supporting women in Engineering and ICT. By learning from the successes and challenges in each country, we can develop more effective strategies to promote gender equity and create a more inclusive environment for women in Engineering and ICT. This may help to retain women in their Engineering and ICT careers.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.089 |
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".