The Evolving Pattern of Occupational Segregation by Race and Gender of Enlisted Personnel in the United States Armed Force (1984-1998)
Bibliographic record
Abstract
The US military has been praised for pursuing policies of integration and affirmative action, but the concept of integration appears to be identified with increased rates of representation and promotion of women and minorities, rather than occupational integration. In this paper trends in the evolution of horizontal occupational segregation of enlisted personnel by race and gender are documented for the four Armed Forces for the period 1984-98, using a multi-dimensional numerical approach developed by Silber and extended by Watts. It is shown that only the Navy achieved occupational integration by gender and race over this period, but that the highly skilled occupations tended to enjoy higher rates of integration (lower increases in segregation) than other groups of occupations within the overall structure of employment by race and gender. These results suggest that further research is required to explore the interaction of practices of recruitment and occupational assignment with the occupational preferences and aptitudes of new applicants for military employment. I am indebted to the Defense Data Center for their cooperation in providing me with US Armed Forces data on employment by gender, race and occupational assignment and attrition and accession data by gender and race.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".