1,720,978 research outputs found
Bias in the Flesh
No full textReplication data for studies 1-3 in "Bias in the Flesh Skin Complexion and Stereotype Consistency in Political Campaigns," see http://poq.oxfordjournals.org/content/early/2015/12/17/poq.nfv046.abstract
Bias in the Flesh
No full textReplication data for studies 1-3 in "Bias in the Flesh Skin Complexion and Stereotype Consistency in Political Campaigns," see http://poq.oxfordjournals.org/content/early/2015/12/17/poq.nfv046.abstract
Replication of Bias in the Flesh
No full textReanalysis data for replication of Bias in the Flesh Study
Replication of Bias in the Flesh
No full textReanalysis data for replication of Bias in the Flesh Study
Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook
No full textReplication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook.
This archive includes:
R analysis code and aggregate data for deriving the main results (e.g., Table S5, S6)
Python code and dictionaries for training and testing the hard-soft news classifier
Aggregate summary statistics of the distribution of ideological homophily in networks.
Aggregate summary statistics of the distribution of ideological alignment for hard content shared by the top 500 most shared websites.
See README.html for a complete description of each file
Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook
No full textReplication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook.
This archive includes:
R analysis code and aggregate data for deriving the main results (e.g., Table S5, S6)
Python code and dictionaries for training and testing the hard-soft news classifier
Aggregate summary statistics of the distribution of ideological homophily in networks.
Aggregate summary statistics of the distribution of ideological alignment for hard content shared by the top 500 most shared websites.
See README.html for a complete description of each file
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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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