Can I have it all? Emerging adult women’s positions on balancing career and family
Bibliographic record
Abstract
Purpose Emerging adult women are actively engaged in career and family explorations, amidst changing opportunities and constraints. The purpose of this paper is to investigate whether such women felt they could balance a high-achieving career and a family life, or what has become known in the popular discourse as women “having it all.” Design/methodology/approach This qualitative study utilized focus groups to explore subjective perceptions of balancing career and family held by emerging adult women. The sample (n=69) comprised female university students in a large Canadian metropolitan area. Findings Thematic analyses unearthed six distinct yet overlapping positions on the possibility of balancing career and family: Optimism (“I can have it all.”), Pessimism (“I cannot have it all.”), Uncertainty (“I am not sure I can have it all.”), Choice (“I don’t want to have it all.”), Pragmatism (“This is what I need to do to have it all.”) and Support (“Will I access the support necessary to have it all?”). Research limitations/implications Limitations include the potential of focus groups to elicit group polarization and to lead participants to censor opinions to conform to conversations. Still, the study reveals more nuanced positions held by women than reported earlier. Originality/value The study extends prior research by revealing the range of positions held by women toward career and family, highlighting women’s understanding of the complex issues involved and showcasing their awareness of the crucial role of social support.
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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 | 0.006 | 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 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".