Bibliographic record
Abstract
The status of American culture in the year 2000 indicates a liberalized change in attitude toward lesbians and gays, particularly in the area of military service. However, the military is staunchly opposed to full inclusion of known lesbians and gays in its ranks and, like other controversial social issues, has failed to consider a plan for implementing full integration in the event current policy should change. The paper seeks to answer the question whether previous social changes within a military institution can provide an experiential basis for prescribing a contingency plan in the event known lesbian and gay persons are granted permission to serve in the US military. The paper concludes that, although past military social transformations cannot provide an exact blue print for integration of lesbians and gays, the experiences can provide a framework. The highly controversial and historic integration of black soldiers in the US military in 1948 and Canada's litigious complete integration of lesbian and gay soldiers in 1992, together provide the social construct to which a change model for large organizations is applied and used for analysis and the paper's conclusions. The paper argues for anticipating change and initiating an early start to the planning process and to shaping operations. Most importantly, the paper argues for visionary leadership. The monograph concludes that a contingency plan is needed, and suggests a conceptual framework for the plan.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".