Outlier-ism is a distinctive research orientation that seeks to promote public value by generating knowledge through systematic study of outliers for use in policymaking
Step 1: identifying relevant outliers according to policy-issue;
Step 2: identifying possible underlying mechanisms that give rise to the outliers both by qualitative and quantitative research;
Step 3: assessing accumulated knowledge about outliers for use in policy design;
•Whether, how and to what extent current policy design and implementation reflects research findings?
Note: moving from outliers to policy poses methodological challenges:
•Establishing a credible relationship between identifiable difference and the outcome of interest
•Determining if the difference is sui generis or something that could be replicated
•Translating the differences into working policies
Types of Outliers
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Role Models
Manifest a desired outcome (“positive deviance”)
Show better than expected outcomes than predicted by the mainstream pattern
Demonstrate or inspire policy aim
First-generation students, Women in STEM, Exit poverty, Scientists policy entrepreneurs
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Stragglers
Worse than expected outcomes
Show lower achievements than is expected according to the mainstream pattern
Demonstrate overlooked social problems
Downward mobility, High SES families’ offspring dropping out or get involved in criminal activities
Retrogress despite complying with current intervention programs (Demonstrate policy failure)
Chronical unemployment, Poverty among working, Marginalized individuals who are involved in harmful behaviors
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Non-compliers
Those whose behavior is inconsistent with policy, yet inspire
May trigger social change, and in turn, policy change
[public] legalizing medical and recreational marijuana, homeschooling, community policing, integration of children with special needs
[officials] street-level divergence (Gofen), guerrilla government (O’leary)
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Pragmatism (Dewey, see Dunn, 2018)
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The need to “recognize, own, and deal with incongruities between social science and design orientations” (Barzelay et al., 2021:12)
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Furnishing usable knowledge (Lindblom and Cohen, 1979)
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Extrapolation problem to bring social mechanisms into smart practice analysis (Bardach, 1998)
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Responds to core questions in Policy Sciences:
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“How to make research more policy analytic” (Weimer, 2012)
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“How to design policy interventions” (Barzelay et al., 2021)
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Related but not identical to reverse engineering
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how to reverse-engineer a practice identified within a “source site”
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how to use what eventuates into the intervention design for a “target site”
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Not necessarily about artificial systems
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Related but not identical to realistic program evaluation (Pawson & Tilley, 1997) which reverse programs
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Accords with the suggestion to consider Design Exemplars as Processes, Not Entities (Barzelay 2007)
