The Organisational Culture of NW Engineering Workplaces: The Influence on Women Engineers
Bibliographic record
Abstract
In England’s North West, engineering continues to be a major industry: employing 90,000 workers, accounting for around a quarter of jobs in the region, and generating £13 billion of the North West’s £44 billion total Gross Domestic Product. However, despite this growth and over 30 years of equality legislation, women in engineering careers in the region remain an insignificant statistical category. Whilst initiatives aimed at increasing female recruitment to the industry have had some, albeit limited effect, retention of female engineers is falling, suggesting that female encounters with organisational cultures in engineering firms may be less than encouraging. The WEWIN project team examined, analysed and contrasted the experiences of men and women working in engineering occupations in the North West, over a period of 12 months. Using participant observation, focus groups, questionnaires and in-depth interviews with engineers, technical directors and HR professionals, the research team explored the broad array of explanations for the persistence of occupational segregation in this industry; and importantly, attempted to ascertain those factors which aid the successful recruitment and retention of female engineers. Beyond palpable sex discrimination, the team identified a series of complex interactions between multifaceted phenomena- ranging from the long hours culture to the gender stereotyping of roles- which can all lead to an early exit from the engineering industry for many women. This paper presents the findings of the WEWIN research, outlines the barriers to effective female participation in the engineering industry, and, importantly, explores possible solutions to this enduring conundrum.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".