In the last two decades, ICTs have played a pivotal role in empowering rural populations in India by making knowledge more accessible. Digital Green (DG) is one such ICT that employs a participatory approach with smallholder farmers to produce instructional videos that encompass content specific to them. With help of human mediators, they disseminate these videos using projectors to improve the adoption of agricultural practices. DG's web-based data tracker stores attendance and adoption logs of millions of farmers, videos screened and their demographic information. We leverage this data for a period of ten years between 2010-2020 across five states in India and use it to conduct a holistic evaluation of the ICT. First, we find disparities in adoption rates of farmers, following which we use statistical tests to identify different factors that lead to these disparities and gender-based inequalities. Second, to provide assistance to farmers facing challenges, we model the adoption of practices from a video as a prediction problem and experiment with different model architectures. Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations. Third, we use SHAP values in conjunction with our model for explaining the impact of various network, content and demographic features on adoption. Our research finds that farmers greatly benefit from past adopters of a video from their group and village. We also discover that videos with a low content-specificity benefit some farmers more than others. Next, we highlight the implications of our findings by translating them into recommendations for community building, revisiting participatory approach and mitigating inequalities. We conclude with a discussion on how our work can assist future investigations into the lived experiences of farmers.
翻译:在过去二十年中,信通技术通过使知识更易于获取,在增强印度农村人口权能方面发挥了关键作用。数字绿色(DG)是这种信通技术,它与小农户一起采用参与式方法,制作包含他们具体内容的教学视频。在人类调解员的帮助下,他们利用项目管理员传播这些视频,以改进农业做法的采用。DG的网络数据追踪器储存数百万农民的出勤和收养记录、视频筛选及其人口信息。我们在印度五个邦之间,在2010-2020年的十年期间利用这些数据,并利用这些数据对信通技术进行整体评估。首先,我们发现农民采用率的差异,在此之后,我们利用统计测试来找出导致这些差异和性别不平等的不同因素。第二,为面临挑战的农民提供援助,我们将采用视频做法作为预测问题,并尝试不同的模型结构。我们的分类方法在五个邦的79%至90%之间,展示了它协助未来调查的潜力。第三,我们利用SHAP的价值观与我们的模型一起,确定导致这些差异和性别不平等的不同因素。我们从以往的网络内容和视频内容中得出了对农民的极大影响。我们从过去研究中获取的收益。我们通过一个网络内容和成果。我们从过去研究中获取了更多的成果。