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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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Underdetermination and evidence-based policy.

Fredrik Andersen1, Elena Rocca2

  • 1Faculty of Health and Welfare, Østfold University College, Halden, Norway.

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|August 11, 2020
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Summary

This study proposes a new method for evaluating safety evidence, especially for stacked genetically modified plants, by adding converse abduction to existing criteria to improve evidence-based policy.

Keywords:
Background assumptionsEvidence evaluationEvidence-based policyEvidential underdeterminationExpert disagreementOntology

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Area of Science:

  • Science and Technology Policy
  • Risk Assessment
  • Biotechnology Regulation

Background:

  • Evidence for technology safety is often insufficient, leading to challenges in policy-making.
  • Genetically modified plants, particularly stacked varieties, present complex safety assessment issues.
  • Existing explanatory approaches to evidence underdetermination have limitations.

Purpose of the Study:

  • To propose an enhanced explanatory approach for evaluating scientific evidence in safety assessments.
  • To address the limitations of existing criteria for selecting among multiple valid explanations.
  • To improve the effectiveness of evidence-based policy for complex technologies.

Main Methods:

  • Reviewing existing explanatory criteria for scientific evidence (transparency, empirical competence, internal consistency, predictive potency).
  • Introducing converse abduction as an additional criterion, considering ontological background assumptions alongside evidence.
  • Applying the enhanced scheme to the specific case of regulating stacked genetically modified plants.

Main Results:

  • The proposed converse abduction criterion helps differentiate between multiple explanations that satisfy traditional criteria.
  • The enhanced approach provides a more robust framework for assessing safety evidence for stacked genetically modified plants.
  • The case study demonstrates the practical application and potential of the refined methodology.

Conclusions:

  • The enhanced explanatory approach, incorporating converse abduction, offers a more nuanced method for evidence-based safety assessment.
  • This approach can lead to more effective and reliable policy decisions regarding novel technologies.
  • Further research is needed to explore the generalizability of this approach across different scientific domains.