Women’s spousal and career identities in male-dominated industries
Bibliographic record
Abstract
There is little known about the importance of women’s identities in terms of both their familial domains and their male-dominated career domains, which have contradictory role demands. In this thesis, I build on the literature about positive social identities in organizations through linking married women who work in male-dominated industries and their self-views regarding their spousal and career identities. I propose that identity conflict mediates the effect of positive spousal and career identities such that when women hold favorable regard for their social identities in their marriage or their career, they experience reduced identity conflict and subsequent increased psychological well-being, career commitment, and relationship satisfaction. I explore spousal support as a moderator whereby when women in male-dominated industries have higher levels of spousal support, their positive identities will further reduce their experiences of identity conflict, leading to higher levels of well-being, relationship satisfaction, and career commitment. The results indicate that women’s positive spousal and career identities are negatively associated with identity conflict. Identity conflict is subsequently negatively associated with psychological well-being and relationship satisfaction. Additionally, both positive career identity and positive spousal identity were found to have a positive indirect relationship with psychological well-being. The moderating role of spousal support was non-significant. Theoretical contributions, practical implications, and an agenda for future research are discussed.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".