Perceived career challenges and response strategies of women in the advanced technology sector
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
The objective of this study is to gain a better understanding of the perceived barriers to career advancement specific to women in the advanced technology sectors. Strategies employed in response to perceived barriers are also examined. Empirical results are based on analysis of qualitative data from a sample of 115 women members of Canadian Women in Technology. Personal-, firm- and industry-level barriers to career advancement were documented. The respondents attributed a high proportion of the challenges they encountered to gender. Respondents were most likely to resolve challenges through personal, or ‘do-it-yourself’, solutions. Few cited firm- or industry-related support structures. While mentoring was identified as a frequently used response strategy through which women address career challenges, the majority of firms in the advanced technology sector lack sufficient numbers of suitable women mentors. The lack of mentorship opportunities is particularly acute for women entrepreneurs. The findings are discussed from the context of contradictions between an industry need to attract and retain entrepreneurial talent and respondents’ perceived career barriers. Industry-level remedial strategies are advanced in the form of: a women's mentoring programme; case studies about successful women entrepreneurs and a website to inform women about career advancement strategies. The programmes were designed by the research team to respond to the challenges cited by women and were implemented in cooperation with the trade association as a critical component of an on-going applied research programme.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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