• The value of data and other non-traditional scholarly outputs in academic review, promotion, and tenure in Canada and the United States

    Juan Pablo Alperin (see profile) , Lesley A. Schimanski, Michelle La, Meredith T. Niles, Erin C. McKiernan
    Evaluation, Education, Higher, Open access publishing, Data sets
    Item Type:
    Book chapter
    Assessment, Higher education, Open access, Open data
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    Academics are regularly involved in a wide range of activities spanning research, teaching and service, and the breadth of necessary outputs for review, promotion, and tenure (RPT) in each category only continues to grow. How do faculty manage their academic careers in the face of such growing sets of demands? Although we know that discussions of research assessment across the academy are increasingly recognizing the need to value the creation of outputs beyond research published in peer-reviewed journals, it is not clear whether these discussions have made their way into formal assessment structures. By analyzing the extent to which non-traditional outputs, including data and software, are mentioned in the RPT documents of a representative set of 129 universities from the United States and Canada, this chapter offers empirical evidence from across many disciplines of which types of faculty work are recognized in the RPT processes, and which are not. We confirm that traditional outputs such as peer-reviewed journal articles, book chapters and monographs are mentioned almost universally, whereas data-related items such as datasets and databases are mentioned only by a fraction of institutions. We find that research-intensive institutions acknowledge more types of research outputs in general, whereas institutions that focus more on undergraduate and master’s degree programs tend to mention fewer forms of scholarship in their RPT guidelines. Within research-intensive institutions, units from the life sciences present a greater range of outputs in the guidelines offered to faculty, including the 15% that explicitly mention data-related outputs. In contrast, none of the academic units in mathematics and physical and social sciences in our sample recognize data-related outputs, and generally recognize fewer forms. Overall, we conclude that many current structures for faculty assessment do not explicitly recognize the increasing complexity and demands of faculty work.
    Although this book chapter is part of a book about Linguistics, the chapter has data spanning all disciplines and fields.
    Published as:
    Book chapter    
    Last Updated:
    3 years ago


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