Young career women debate question of “having it all”
Bibliographic record
Abstract
Purpose Researchers felt previous research had not addressed the complexity of young women’s views on whether they can have it all – which essentially means a great career at the same time as being a great mother. They identified six different positions, with either extreme represented by the optimist and pessimist viewpoints. Design/methodology/approach There were interviews with 14 small focus groups of between two and eight members. All were emerging adults at a Canadian university and on the verge of moving into careers. The focus was on the challenges of work–family balance, especially in managerial roles. Findings Six common positions emerged - optimism, pessimism, uncertainty, choice, pragmatism, and support. Optimists were certain they could achieve balance and “have it all” even if it entailed a slight struggle. In total contrast to the optimists, the pessimists’ position was that no one could have it all – both a really good job and a healthy family life – and it was always necessary to give something up. Other positions were more nuanced. Originality/value Previous studies have tended to be quantitative and come up with much less-nuanced results. The study enabled researchers to tease out the complexity of women’s views.
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 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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".