Automated tools for syntactic complexity measurement are increasingly used for analyzing various kinds of second language corpora, even though these tools were originally developed and tested for texts produced by advanced learners. This study investigates the reliability of automated complexity measurement for beginner and lower-intermediate L2 English data by comparing manual and automated analyses of a corpus of 80 texts written by Dutch-speaking learners. Our quantitative and qualitative analyses reveal that the reliability of automated complexity measurement is substantially affected by learner errors, parser errors, and Tregex pattern undergeneration. We also demonstrate the importance of aligning the definitions of analytical units between the computational tool and human annotators. In order to enhance the reliability of automated analyses, it is recommended that certain modifications are made to the system, and non-advanced L2 English data are preprocessed prior to automated analyses.

Private universities’ governing board members, commonly described as “the guardians of universities'', play a vital role in directing and connecting the universities with external organizations. However, despite their important roles, we know very little about who are board members. Hence, this study seeks to bridge that knowledge gap by examining the governing board’s demographics, more specifically their social backgrounds, professional affiliations, managerial positions, and political involvement. Fifteen US non-profit private universities with the largest philanthropic donations are selected for this study. In addition to the gender inequality in board representation, our main finding is that people with business backgrounds are pre-dominant in non-profit university boards. This provides empirical evidence for the much-discussed affinity between higher education and businesses, and thus urges scholars, policymakers as well as the public in general to rethink the role of higher education.

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