Enabling Scientific Crowds: The Theory of Enablers for Crowd-Based Scientific Investigation
Jorge Faleiro

TL;DR
This paper introduces the Theory of Enablers, a framework for crowd-based scientific investigation that enhances reproducibility, transparency, and collaboration by leveraging human diversity and computational methods.
Contribution
It defines specific cognitive and non-cognitive enablers that enable structured, crowd-powered scientific procedures, addressing current limitations in scientific reproducibility and transparency.
Findings
Defines a set of enablers for crowd-based scientific investigation
Proposes a framework leveraging human diversity and computational tools
Highlights potential for improved reproducibility and transparency
Abstract
Evidence shows that in a significant number of cases the current methods of research do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of critical decisions at all levels, from personal investment choices to overreaching global policies, rely on some variation of try-and-error and are mostly non-scientific by definition. We lack transparency for procedures and evidence, proper explanation of market events, predictability on effects, or identification of causes. There is no clear demarcation of what is inherently scientific, and as a consequence, the line between fake and genuine is blurred. This paper presents highlights of the Theory of Enablers for Crowd-Based Scientific Investigation, or Theory of Enablers for short. The Theory of Enablers assumes the use of a next-generation investigative approach leveraging forces…
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Taxonomy
TopicsComplex Systems and Time Series Analysis
