Bibliographic record
Abstract
A major challenge for contemporary military policy makers has been the integration of gender into policy. Since 2000, Canada has opened all military roles (including combat and naval ones) to women. This includes Canadian participation in peacekeeping operations (PKO), an essential part of the national identity. From Lester B. Pearson’s work with the United Nations during the Suez crisis to missions in Haiti, Cyprus and Bosnia, Canada has been a part of multilateral operations to support peaceful resolution of conflicts throughout the 20th and 21st centuries. Tens of thousands of Canadians have served in over 40 peacekeeping and peace support operations since the 1960s (Veterans Affairs Canada, 2011, 2012). Despite the freedom to participate, women still constitute a significant minority of Canadian and UN peacekeeping forces. Yet, the nature of PKO and the roles Canadians play today has changed significantly since the end of the Cold War. The impact of armed conflict on women has dramatically increased and the violation of women’s rights has become a focal point in most modern conflicts. Due to the changes in conflicts and the role of a peacekeeper, the integration of gender into all aspects of peacekeeping operations would significantly increase their operational effectiveness. I will begin by explaining the types of modern peacekeeping operations, defining the concept of gender and discussing how operational effectiveness of peacekeeping is measured. Utilizing this definition of operational effectiveness, this presentation will explore how the inclusion of gender will increase operational effectiveness from two perspectives – that of the peacekeeper and that of the victim.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".