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Structural Design and Manufacturing of a Cruiser Class Solar Vehicle
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Structural problems require structural solutions.

Nina Strohminger1, Olúfẹ Mi O Táíwò2

  • 1Legal Studies and Business Ethics Department, University of Pennsylvania, Philadelphia, PA humean@wharton.upenn.edu; www.ninastrohminger.com.

The Behavioral and Brain Sciences
|August 30, 2023
PubMed
Summary
This summary is machine-generated.

Behavioral science often uses individual-level analysis, limiting policy and progress. Structural changes in training, peer review, and funding are needed to address this bias and advance the field.

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Area of Science:

  • Behavioral Science
  • Social Psychology
  • Policy Interventions

Background:

  • The dominant individual-level analysis (
  • i-frame
  • ) in behavioral science is criticized for limiting policy interventions and scientific advancement.
  • This reliance on the i-frame may be perpetuated by inherent structural biases within the scientific community.

Purpose of the Study:

  • To extend the critique of the individual-level analysis in behavioral science.
  • To identify and analyze the structural factors that perpetuate the i-frame bias.
  • To propose necessary structural changes to overcome these limitations.

Main Methods:

  • Critical analysis of existing literature on behavioral science methodologies.
  • Theoretical extension of Chater & Loewenstein's critique.
  • Examination of systemic issues within scientific training, peer review, and funding structures.

Main Results:

  • The i-frame bias in behavioral science is not only a methodological preference but is reinforced by structural elements.
  • Training programs, peer review processes, and research funding mechanisms systematically favor individual-level explanations.
  • These structural factors hinder the development of more comprehensive and impactful behavioral interventions.

Conclusions:

  • Addressing the limitations of the i-frame requires fundamental structural reforms within behavioral science.
  • Changes in scientific training, peer review standards, and grant allocation are crucial for progress.
  • Overcoming structural biases will enable richer policy interventions and accelerate scientific discovery.