A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science

Autor: M. V. Eitzel
Rok vydání: 2021
Předmět:
Manifesto
Decision support system
decision support
mixed methods
Computer science
science and technology studies
Best practice
Science
Big data
triangulation
Participatory modeling
data biography
reproducibility crisis
critical theory
big data
quantitative-qualitative analysis
citizen science
Citizen science
algorithmic injustice
qualitative analysis
critical data science
History of science
high variety big data
reproducibility
community-based participatory research
legal
participatory modeling
quantitative analysis
situated knowledge
business.industry
epistemology
General Medicine
community and citizen science
science and justice
Data science
ethical
data justice
machine learning
history of science
Critical theory
high volume big data
interdisciplinary
and social implications
data science
business
Zdroj: Research Ideas and Outcomes, Vol 7, Iss, Pp 1-29 (2021)
Popis: In the face of the "crisis of reproducibility" and the rise of "big data" with its associated issues, modeling needs to be practiced more critically and less automatically. Many modelers are discussing better modeling practices, but to address questions about the transparency, equity, and relevance of modeling, we also need the theoretical grounding of social science and the tools of critical theory. I have therefore synthesized recent work by modelers on better practices for modeling with social science literature (especially feminist science and technology studies) to offer a "modeler’s manifesto": a set of applied practices and framings for critical modeling approaches. Broadly, these practices involve 1) giving greater context to scientific modeling through extended methods sections, appendices, and companion articles, clarifying quantitative and qualitative reasoning and process; 2) greater collaboration in scientific modeling via triangulation with different data sources, gaining feedback from interdisciplinary teams, and viewing uncertainty as openness and invitation for dialogue; and 3) directly engaging with justice and ethics by watching for and mitigating unequal power dynamics in projects, facing the impacts and implications of the work throughout the process rather than only afterwards, and seeking opportunities to collaborate directly with people impacted by the modeling.
Databáze: OpenAIRE
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