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This study argues that collaboration networks can look more or less scale\u2010free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4 M \u223c 9.6 M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename\u2010initial\u2010based name matching, tend to produce degree distributions better fitted to power\u2010law slopes with the typical scaling parameter (2 &lt; <jats:italic>\u03b1<\/jats:italic> &lt; 3) than networks disambiguated by more accurate algorithm\u2010based methods. Such tendency is observed across collaboration networks generated under various conditions such as cumulative years, 5\u2010 and 1\u2010year sliding windows, and random sampling, and through simulation, found to arise due mainly to artefactual entities created by inaccurate disambiguation. 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