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Record W7011291686

The Lived Experience of Career Sponsorship of Canadian Female Executives

2024· article· en· W7011291686 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks (Walden University) · 2024
Typearticle
Languageen
FieldEngineering
TopicPhysics and Engineering Research Articles
Canadian institutionsnot available
Fundersnot available
KeywordsLived experienceQualitative researchInterpretative phenomenological analysisCareer developmentBalance (ability)Executive summaryCareer planning
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.216
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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