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Review Article
Research Ideas and Outcomes 7: e71553
https://doi.org/10.3897/rio.7.e71553 (08 Sep 2021)
https://doi.org/10.3897/rio.7.e71553 (08 Sep 2021)
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Article metadata
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Introduction
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Technical Concerns with High-Volume Big Data
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Context Issues with High-Variety Big Data
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Limitations of and Justice Issues with Predictive Models
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Data Resources for Review
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Research Questions
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Evaluation/validation Process
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Discussion
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Theme 1: Contextualizing Modeling
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Practice 1a) Epistemic consistency: Know the epistemology that underlies methods and draw conclusions appropriately
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Practice 1b) Data biographies: Report the details and learn the ethnography of modeling
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Practice 1c) Mixed-methods analysis: Frame modeling processes as including quantitative and qualitative components
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Theme 2: Collaboration with Other Partial Knowledges
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Practice 2a) Triangulation: Because no modeling technique is truly objective, seek ways to check models using other knowledge
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Practice 2b) Uncertainty as openness: Reframe uncertainty as an invitation for collaboration rather than failure
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Practice 2c) Interdisciplinary fluency: Be aware of epistemological, normative, and vocabulary differences in diverse collaborations
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Theme 3: Engaging with justice implications of modeling processes
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Practice 3a) Power dynamics: Watch for and work to mitigate unjust interactions in collaborations
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Practice 3b) Impacts and implications: Engage with what the work does in the world
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Practice 3c) Community-based modeling: Find opportunities to collaborate directly with people who will be affected by modeling results
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Conclusions
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Acknowledgements
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References
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Supplementary files
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