Secrecy, Uncertainty, and Trust: The Gendered Nature of Back-Channel Peace Negotiations
Bibliographic record
Abstract
Abstract Back-channel negotiations are commonplace in peace negotiations and can serve as crucial mechanisms for reaching agreements. While there has been a moderate increase in scholarship examining back-channel negotiations in the last two decades, none has explored the gendered nature of these spaces. This article analyzes how and why back-channel negotiations are highly gendered processes and why their gendered nature matters for sustainable peace. We begin with a review of the current literature on back-channel negotiations and discuss how and why they are critical mechanisms in peace negotiation and agreement processes. Next, we show how women’s inclusion in peace negotiations and agreement practices matters for sustainable peace. Thereafter, we discuss how secret negotiation spaces are infused with gendered power and masculine logics of war and peace. We argue that three key features of back-channel negotiations—secrecy, uncertainty, and limited trust—come together to create an echo chamber of hypermasculinity ideas, values, styles, and norms that prevent women from achieving descriptive and substantive representation inside fundamental secret negotiation spaces. This article adds to the developing literature on back-channel negotiations and helps us better understand how and why women and their interests are regularly excluded from peace processes despite the global prominence of the United Nations’ Women, Peace, and Security agenda.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".