With or without you: Family and Career‐Work in a Demanding and Precarious Profession
Bibliographic record
Abstract
Abstract Family arrangements are crucial to people’s abilities to meet the high demands of professional careers; but most scholarship has examined stable, highly remunerated professions. To understand the relationship between career and family within the increasing number of precarious professions, we analyse interviews with 102 journalists. We discover two broad types of career‐work practices these professionals employ to engage family in their careers: career‐family positioning (i.e., crafting a narrative of how career and family relate) and career‐family resourcing (i.e., generating resources from family for career or vice‐versa). Together, these practices touch more family members – spouses, children, parents, siblings, and extended family members – and involve a wider range of resources than documented in stable fields. By piecing together variations of these practices, professionals construct career strategies that address their difficult context in different ways. Two strategies largely accept the demands and precarity, by prioritizing career and drawing on family , or prioritizing career and forgoing family. A third, prioritizing family over career, involves defying the demands. Gender does not clearly influence which career strategy people pursue. These findings advance scholarship on career and family in the professions, social‐symbolic work, and contribute to careers research more broadly.
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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 | 0.003 | 0.007 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".