Homemaker or Career Woman: Life Course Factors and Racial Influences among Middle Class Americans
Bibliographic record
Abstract
This paper examines the effect of life course factors on the decision by American college educated women to stay at home or continue their careers after they have children. Data come from interviews with 48 white and African-American college-educated women that covered major events from childhood to the present, along with ideas for the future. Interviews were coded in terms of four major themes: identity, relational style, motivation, and adaptation. Findings revealed that identity as a mother was different for the two groups-central for the homemakers but combined with work for the career women. In relational style, homemakers relied on husbands primarily as breadwinners; career women relied on them for help with household work and childrearing. Motivation and rewards of homemakers were centered around mothering, but career women were focused on achievement and recognition at work. The patterns were similar for white and black women, but African-American homemakers were pioneers in staying at home whereas white homemakers were following tradition. Career women of both races used their sense of being outstanding to conquer disadvantage-the black women to defy racial discrimination, the white women to rise from humbler origins or to overcome a disability. In sum, holding constant age, race, marital status, and social class, there are striking differences in the life course of individuals that account for major variants in marriage patterns and women’s roles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".