Choosing mothering and entrepreneurship: a relational career-life process
Bibliographic record
Abstract
Purpose To date, research on women’s entrepreneurship has largely been focused on how gender roles may constrain the venture process, or cause role conflicts for women pursuing an entrepreneurial career. While acknowledging the validity of such perspectives, the purpose of this paper is to apply a broader perspective of career-life development, answering the call for a more nuanced and embedded understanding of an entrepreneurial career. Design/methodology/approach This paper presents a constructionist, relational analysis of the experiences of 13 Canadian women who started their business following the life transition to motherhood. Interview data were coded using grounded theory methods. Findings The conceptual model captures the influence of the mothering role in shaping the transition into entrepreneurship, illuminating the reciprocal relational processes of context, choice and outcomes in the career-life development of mother entrepreneurs. Research limitations/implications While this is a small sample, and findings are not generalizable, application of relational theory of career-life offers implications for supporting women’s transition to, and continued success in, entrepreneurship. Practical implications Career theory offers practical application to the management of mother entrepreneurs’ career-life development. Originality/value To date, there has been limited application of career theory to entrepreneurship, particularly to understanding the gendered, relational career-life experiences of mother entrepreneurs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".