Comparison of Women's Policies in Six International Navies
Bibliographic record
Abstract
The present study compares policies, programs, and practices relating to women in six international navies. Navies from the following nations are included: the United States, Australia, Brazil, Canada, Sweden, and the United Kingdom. Information is drawn from answers by representatives of the six international navies to a detailed questionnaire fielded from May through September 2014. The questionnaire covers eight topic areas: General Information; Maternity/Paternity Issues; Deployments; Assignment; Marriage; Career Path & Development; Navy Policy Development; and Other Information. Additionally, project researchers organized and advised three Master’s thesis projects at the Naval Postgraduate School; these are reported separately. Questionnaire responses are catalogued and compared in 23 tables. Results reveal a similar emphasis on family, the flexible workplace, and various initiatives to encourage the recruiting and retention of highly-qualified women. Selected “best practices” are also identified. A preliminary factors model is introduced for future use in identifying and comparing international policies and practices. Recommendations for further research include expanding the study to include more international navies, continuing to evaluate Life-Work Balance and the flexible workplace, refining the factors model for practical application, and comparing the policies of international navies on women’s equity and safety.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".