使用认知模型来理解来自公民科学家的众包数据
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The HoneyComb Paradigm for Research on Collective Human Behavior
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Alex Thorpe1, Oliver Kelly2, Alex Callen2
1School of Psychological Sciences, University of Newcastle, Callaghan, Australia.
Behavior research methods
|November 29, 2023
概括
公民科学家分析了来自录音的青呼叫. 一个文化共识模型准确地评估了他们的集体发现,验证了众包数据,用于监测受威胁物种,而不需要先前的专家知识.
科学领域:
- 生态生态学 生态生态学
- 生物声学是一种生物声学.
- 保护生物学 保护生物学
背景情况:
- 监测受威胁物种产生了大量的声学和视觉数据,需要广泛的人类或机器分析.
- 用于数据分析的机器学习算法需要大量的标记训练数据集,这些数据集通常是不可用的.
- 公民科学或众包提供了一个可扩展的解决方案,但由于不同专业知识和每个参与者的有限数据暴露,在数据质量评估方面面临挑战.
研究的目的:
- 应用文化共识理论,一种定量认知模型,评估来自公民科学家分析澳大利亚青呼声的众包数据的可靠性.
- 确定共识模型是否可以准确地估计公民科学家的能力和音频录音的特征,即使没有已知的基本真相.
- 评估基于模型的分析对有效选大型生态数据集的有用性.
主要方法:
- 利用文化共识理论来分析来自参与数百名公民科学家的众包项目的实证和模拟数据.
- 公民科学家的任务是识别1260个音频录音中9种澳大利亚青物种的存在.
- 将模型的得出的共识与相同音频录音的专家评估进行了比较.
主要成果:
- 文化共识模型成功估计了公民科学家队列和音频录音的特征.
- 在来自公民科学家数据的共识和专家对录音的编码之间发现了重要的一致性.
- 该模型表明,众包分析可以产生可靠的见解,即使基本真相最初是未知的.
结论:
- 众包数据分析,当使用文化共识理论建模时,可以成为大规模生态监测的可靠方法.
- 这种方法为预先选大型数据集提供了有价值的工具,优化了专家在保护工作中的时间和资源的分配.
- 该研究验证了公民科学和定量建模的使用,以有效监测受威胁物种.
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