The Lived Experience of Career Sponsorship of Canadian Female Executives
Bibliographic record
Abstract
Women represent almost half of the Canadian labor population, but less than 6% of women advance to executive leadership. This is problematic because previous studies showed gender balance has been proven to be good for business, but with not enough women advancing in the leadership pipeline, business performance will continue to suffer. The purpose of this qualitative phenomenological study was to explore the lived experiences of Canadian female executives regarding how career sponsorship may have influenced their career advancement to executive management. Role congruity theory provided the framework for the study. Eleven Canadian female executives participated in semistructured interviews to share their personal lived experience of career sponsorship. Findings from the modified van Kaam data analysis indicated all female executives had multiple informal career sponsorship experiences and their sponsors helped advocate for and propel their career to executive leadership. Themes included sponsors are champions, sponsorees lived up to expectations, sponsorship reciprocity, succession planning, paying it forward, and no-sponsor-no-advancement. Recommendations include urging executives and young professionals to forge an informal sponsorship to support gender balance in executive management. Findings may inspire positive social change by informing women and other professionals, organizations, and policymakers regarding the impact of career sponsorship.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".