Bibliographic record
Abstract
Men, Militarism, and UN Peacekeeping: A Gendered Analysis. By Sandra Whitworth. Boulder, CO: Lynne Rienner, 2004. 225p. $49.95. The rapid expansion in United Nations peacekeeping operations in the 1990s has inevitably led to official and scholarly scrutiny of their efficacy and shortcomings. Sandra Whitworth offers a critical eye and questions of gendered analysis by studying the subjects of the UN's peacekeeping operation in Cambodia, whose presence often meant greater insecurity as well as the (mis)conduct of soldiers from Canada—a country historically linked to major peacekeeping contributions—in Somalia. Using these two case studies, Whitworth highlights a basic contradiction inherent in peacekeeping that stems from its heavy reliance on soldiers whose training emphasizes masculine traits of violence, homophobia, racism, and aggression, yet whose tasks as peacekeepers require limiting violence to self-defense and providing a benign, altruistic presence. She uses gender analysis to examine these contradictions, to challenge peacekeeping's association with alternatives to military violence, to show how peacekeeping forces can increase local people's insecurity, rather than alleviating it, and to critique the use of soldiers for missions requiring unsoldierly skills.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".