The risks of child recruitment by non-state armed groups are geographically, temporally and contextually situated. There are multilayered, multivariate arrays of risk factors associated with non-state armed groups, with conflicts, and with contexts. Using Bayesian network modelling with a global dataset of non-state armed group child recruitment practices between 2010 and 2022, we demonstrate the theoretical and practical importance of adopting a situational perspective to understand child recruitment risks. Methodologically, we demonstrate a robust model-checking process that checks the adequacy of our data, the magnitude and direction of estimated effects, and shows greater than 80% accuracy in predicting child recruitment by non-state armed groups. We review and contrast our approach with standard general linear modelling used in quantitative child recruitment research over the past two decades. Through adopting a situated orientation, and applying analytical tools appropriate to that orientation, we challenge and extend existing theory and propose new theoretical insights on child recruitment risks. We show how important violence is as a predictor of child recruitment risks and, using a new measure of fighting force efficacy, show that, contrary to published theory, less effective non-state armed groups were more likely to recruit children than more effective ones. But even these most notable results we show to vary markedly across situations.

中文翻译：

---

关于招募和使用儿童的可靠知识声明：实证视角


非国家武装团体招募儿童兵的风险在地理、时间和背景上都存在。与非国家武装团体、冲突和背景相关的风险因素存在多层次、多变量。使用贝叶斯网络模型和 2010 年至 2022 年间非国家武装团体儿童招募实践的全球数据集，我们证明了采用情境视角来理解儿童招募风险的理论和实践重要性。在方法上，我们展示了一个强大的模型检查过程，该流程检查我们数据的充分性、估计效果的大小和方向，并在预测非国家武装团体招募儿童方面显示出超过 80% 的准确率。我们回顾了我们的方法，并将其与过去二十年定量儿童招募研究中使用的标准一般线性模型进行了对比。通过采用定位导向，并应用适合该导向的分析工具，我们挑战和扩展现有理论，并提出关于儿童招募风险的新理论见解。我们展示了暴力作为儿童招募风险预测指标的重要性，并使用一种新的战斗部队效能衡量标准表明，与已发表的理论相反，效率较低的非国家武装团体比效率较高的非国家武装团体更有可能招募儿童。但即使是这些最显着的结果，我们也在不同情况下会有很大差异。