How Self-Employed Women with Children Manage Multiple Life Roles / Illustration de la façon dont les mères travailleuses autonomes gèrent plusieurs rôles de vie
Bibliographic record
Abstract
Abstract Although there is a strong body of existing research on women’s career-life development and on women’s entrepreneurship, there is a lack of understanding of the specific experiences of mother entrepreneurs. This grounded theory study addresses the question of how self-employed women with children manage their multiple life roles. The core category “Keeping Going” illustrates a basic social process that is recursively fuelled and affected by 7 key properties: feeling supported, making choices, adapting creatively, remembering the push, remembering the pull, envisioning the future, and living my values. Implications for further research and counselling practice are discussed. Résumé Malgré qu’il existe de nombreuses études sur le développement de carrière chez les femmes et sur l’entrepreneuriat au féminin, il subsiste des lacunes dans la compréhension des expériences spécifiques aux mères entrepreneures. Cette étude théorique à base empirique aborde la question de la gestion des multiples rôles de vie chez les mères travailleuses auto- nomes. La catégorie de base « Keeping Going » (poursuivre sans relâche) illustre la démarche sociale fondamentale qui est alimentée et touchée de façon récursive par 7 propriétés clés : se sentir appuyée, faire des choix, s’adapter de manière créative, se rappeler la poussée, se rappeler la traction, imaginer l’avenir et vivre ses valeurs. L’article présente une discussion des implications pour de plus amples recherches et pour la pratique du counseling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".