从民意中筛选更深入的见解:面向众包和大数据的项目改进

Jean Marie Tshimula, M. M. Njuguna, Thierry Roger Bayala, Mbuyi Mukendi Didier, Achraf Essemlali, Hugues Kanda, Numfor Solange Ayuni
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引用次数: 1

摘要

多年来,在为群众提供社会服务方面,似乎存在着一种单向的自上而下的决策方式。这常常导致在不知情的情况下做出糟糕的决定,其结果不一定符合需求。同样,从基层来说,要把意见传达到执政当局(决策机关)也是一个挑战。因此,政府制定了旨在解决社会关切的目标,但在政府的努力与社会需求不协调的情况下,这些目标往往不会取得预期的效果。通过听取和考虑公众意见,可以更好地了解社会需求,并确定解决这些问题的优先事项。因此,本文提出了一个基于优先级的投票模型,用于政府收集民意数据,并利用众包和大数据分析提供建议,以推动政府朝着正确的方向努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sifting for Deeper Insights from Public Opinion: Towards Crowdsourcing and Big Data for Project Improvement
Over the years, there seems to be a unidirectional top-down approach to decision-making in providing social services to the masses. This has often led to poor uninformed decisions being made with outcomes which do not necessarily match needs. Similarly from the grassroots level, it has been challenging to give opinions that reach the governing authorities (decision-making organs). The government consequently sets targets geared towards addressing societal concerns, but which do not often achieve desired results where such government endeavors are not in harmony with societal needs. With public opinions being heard and given consideration, societal needs can be better known and priorities set to address these concerns. This paper therefore presents a priority-based voting model for governments to collect public opinion data that bring suggestions to boost their endeavors in the right direction using crowdsourcing and big data analytics.
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