Worth Less?: Why Men (and Women) Devalue Care-Oriented Careers
Bibliographic record
Abstract
In the present research, we applied a goal-congruity perspective – the proposition that men and women seek out roles that afford their internalized values (Diekman, et al., 2017) – to better understand the degree to which careers in healthcare, early education, and domestic roles (HEED; Croft, Schmader, & Block, 2015) are devalued in society. Our first goal was to test the hypothesis that men, relative to women, are less interested in pursuing HEED careers in part because they are less likely than women to endorse communal values. A second, more novel goal was to extend goal congruity theory to examine whether gender differences in communal values also predict the belief that HEED careers add worth to society and are deserving of higher salaries. In three studies of undergraduate students (total N = 979), we tested the predictive role of communal values (i.e. a focus on caring for others), as distinct from agentic values (i.e. a focus on status, competition, and wealth; Bakan, 1966). Consistent with goal congruity theory, Studies 1 and 2 revealed that men’s lower interest in adopting HEED careers, such as nursing and elementary education, was partially mediated by men’s (compared to women’s) lower communal values. Extending the theory, all three studies also documented a general tendency to see HEED as having relatively lower worth to society compared to STEM careers. As expected, communal values predicted perceiving higher societal worth in HEED careers, as well as supporting increases in HEED salaries. Thus, gender differences in communal values accounted for men’s (compared to women’s) tendency to perceive HEED careers as having less societal worth and less deserving of salary increases. In turn, gender differences in perceived societal worth of HEED itself predicted men’s relatively lower interest in pursuing HEED careers. In no instance, did agentic values better explain the gender difference in HEED interest or perceived worth. These findings have important implications for how we understand the value that society places on occupations typically occupied by women versus men.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".