1,720,967 research outputs found
Replication Data for: Can Details Depoliticize?
No full textReplication data for: Can details depoliticize
Replication Data for: How Do Public Managers Learn from Performance Information? Experimental Evidence on Problem Focus, Innovative Search, and Change
No full textReplication Data for: How Do Public Managers Learn from Performance Information? Experimental Evidence on Problem Focus, Innovative Search, and Change.
Replication data for Study 2. Data will not be available for Study 1 due to a confidentiality agreement
Replication data for "Who Reacts Negatively to Reminders?"
No full textReplication data for "Who Reacts Negatively to Reminders?
Replication Data for: "Hansen, Jesper and Lars Tummers. 2020. A Systematic Review of Field Experiments in Public Administration. Public Administration Review"
No full textReplication materials for "A Systematic Review of Field Experiments in Public Administration
Problems in Supervision - An Analysis of Challenges, Explanations, and the Potential for Strengthening Municipal Oversight in Daycare Services
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The Politics of Evidence Selection
No full textIn an era often described as post-truth politics, it is crucial to understand how politicians engage with evidence. We argue that politicians selectively engage with evidence consistent with their party’s priorities. We test this argument by presenting 1,822 candidates in the 2024 Austrian national election with contrasting pieces of high-quality evidence on the same salient migration policy reform. We find that candidates from ideologically extreme parties – particularly the far-right populist Freedom Party of Austria (FPÖ) – select evidence consistent with their party’s priorities. Analysis of public opinion data (N = 1,428) further shows that far-right supporters approve of this selective consideration of evidence. Our findings suggest that growing far-right electoral strength may amplify the reliance on supportive evidence, increasing the risk of ill-informed policy decisions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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