从定性开放科学的角度重新思考透明度和严谨性

Crystal N. Steltenpohl, Hilary Lustick, M. S. Meyer, L. E. Lee, Sondra M. Stegenga, Laurel Standiford Reyes, R. Renbarger
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引用次数: 5

摘要

关于开放科学透明度的讨论主要集中在共享数据、材料和编码方案上,特别是当这些实践与可重复性相关时。这种相当定量的透明度观点并不符合所有的科学方法。事实上,定性研究人员也非常关心知识是如何产生的,什么因素影响研究过程,以及如何分享这些信息。阐明研究人员的背景和角色使研究人员能够考虑他们对研究过程和数据解释的影响,从而增加透明度和严谨性。研究人员可能会以各种方式进行定位和反思,透明地分享这些步骤可以让读者对结果和整个研究得出自己的明智结论。如果研究人员不仔细考虑这些标准的影响,那么对所有研究强加一套有限的、定量的标准可能会对研究人员和他们合作的社区造成伤害。我们的论文将讨论避免围绕透明度(例如,始终共享数据)的强烈默认的重要性,并以先前围绕定性开放科学的工作为基础。我们探索如何在我们的研究的各个方面的透明度可以帮助自己预测和确认我们的工作的严谨性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rethinking Transparency and Rigor from a Qualitative Open Science Perspective
Discussions around transparency in open science focus primarily on sharing data, materials, and coding schemes, especially as these practices relate to reproducibility. This fairly quantitative perspective of transparency does not align with all scientific methodologies. Indeed, qualitative researchers also care deeply about how knowledge is produced, what factors influence the research process, and how to share this information. Explicating a researcher’s background and role allows researchers to consider their impact on the research process and interpretation of the data, thereby increasing both transparency and rigor. Researchers may engage in positionality and reflexivity in a variety of ways, and transparently sharing these steps allows readers to draw their own informed conclusions about the results and study as a whole. Imposing a limited, quantitatively-informed set of standards on all research can cause harm to researchers and the communities they work with if researchers are not careful in considering the impact of such standards. Our paper will argue the importance of avoiding strong defaults around transparency (e.g., always share data) and build upon previous work around qualitative open science. We explore how transparency in all aspects of our research can lend itself toward projecting and confirming the rigor of our work.
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