Come a Long Way and a Long Way to Go: UNSCR 1325 and Women's Participation in Peace-Making
Bibliographic record
Abstract
To be powerful and influential, one can argue, requires not just representation but presence, and not just presence, but meaningful, empowered presence. In 2016, there were only ten women serving as heads of states and nine serving as heads of government, and women held only 22 % of seats in parliaments around the world. Despite huge effort and promotion, women candidates for the top jobs at the United Nations (UN) and the US Government failed to prevail. Peace processes, in particular, as they “provide key opportunities for major reforms that transform institutions, structures, and relationships in societies affected by conflict or crises,” are instrumental for women's empowerment and for their consideration in the construction of their post-conflict society. According to a study by UN Women based on 31 major peace processes occurring between 1992 and 2011, women represented 2.4 % of chief mediators (although the UN itself has never appointed a woman as chief mediator), 4 % of peace agreement signatories, and 9 % of negotiators in formal peace processes. Most of the time, this low representation of women in peace negotiations is the result of passive – as opposed to deliberate – exclusion, but as some feminist writers have clearly underlined, gender-neutrality often corresponds to gender-blindness. Traditionally, women are very much involved in informal peace negotiations at the grassroots level and within civil society initiatives, and in particular in disarmament processes; this contribution is now widely documented and recognized. But as is well known, women's participation in formal peace negotiations remains very marginal. Moreover, even when women are included, their viewpoints are often sidelined, as they are perceived to lack relevant qualifications, credibility or simply power. Many hypotheses have been proposed to explain the dramatic underrepresentation of women in formal peace processes, starting with a lack of women in the traditional institutional “pipelines” to mediation and negotiation. For example, women are still a minority within governments and armies, and in the military and political wings of armed groups, and yet the belligerents ‘ representatives are generally perceived to be the most crucial actors in peacemaking – at least in the prevailing concept of peace-making – to the expense of other groups, mostly civil society.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".