Three Point Nine Percent Female: A Review of the Barriers to Increasing Female Troops Participation on United Nations Peace Operations
Bibliographic record
Abstract
Since UNSCR 1325 (2000) urged member states to ensure increased representation of women at all levels, the UN has made marginal progress improving the gender balance of troops deployed on UNPO. Despite ambitious targets for increasing the percentage of female troops in these operations, as of January 2019 women made up only 3.9% of all troops deployed on UNPO, up from 1.8% in 2006. Attempts to assess the barriers to improving the gender balance of personnel tend to consider all types of military personnel, police and civilian staff involved in UNPO, rather than focusing on the specific challenges of meeting gender targets for troops. In order to fill the current policy and research gap, this paper considers the challenges to improving the gender balance of troops. Existing studies highlight four possible explanations for the failure to make quicker progress: (1) there are not enough female troops available to contribute to UNPO; (2) UN and UN member states policies and procedures discourage women’s inclusion; (3) the gendered predispositions of decision makers and societal norms portray women as needing protection rather than as protectors; and (4) there are few incentives offered to both female troops and UN member states. This paper will evaluate each of these explanations as well as solutions that have been proposed to overcome existing barriers to female troop participation in UNPO. It will conclude with a set of recommendations, calling on the UN and UN member states to: improve recruitment and retention of women in national armies; eliminate unnecessary policies discouraging women’s inclusion; directly challenge gendered predispositions that neglect to see women as protectors; and provide financial incentives for UN member states that contribute female troops. The paper concludes by addressing the implications of its research for Canada and analyzing current actions being taken to improve the gender balance of troops deployed on UNPO.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".