INTEGRATING WOMEN IN THE ARMED FORCES: TWO WAYS FORWARD
Bibliographic record
Abstract
Despite the participation of women in the armed forces for many decades, resistance to integrating women fully into the armed forces still exists. Women have contributed in combat previously, but they have been released and assigned traditional roles after the end of conflicts. Despite the record of women’s valuable service, doubt about women’s integration in the armed forces continues, and the participation of women in various countries’ armed forces differs both in numbers and roles. In this connection, this research identifies the major debates surrounding the full integration of women in the armed forces. The thesis also identifies how technological changes and changes in the nature of war itself, as well as legal provisions conducive to the integration of women in the military, have increased the participation of women in the military. Through case studies of the armed forces of Canada and Jordan, the thesis reveals that cultural differences in different countries preclude a single approach to integrating women in the military. Moreover, acknowledging that the legislative provisions of a country, its cultural norms, and the policies of a nation’s armed forces affect the integration of women in the military, the research makes some recommendations to increase the participation of women in the military.
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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".