Gender Bias and the Journal of Roman Studies

Abstract

Reflecting on present unease about structural biases in the discipline, and aiming to offer a data-rich response to some recent criticisms of this Journal, the Editorial Board has undertaken a study of the representation of female scholars in the Journal of Roman Studies. To that end, we have gathered data on publications, submissions and JRS Editorial Board membership for the past fifteen years, from Volume 95 (2005) through to the present volume, Volume 109 (2019). The data are set out in the final section (VII), following a brief review of the main results. Our goal here is neither to present a definitive analysis, nor to offer a commentary on the underlying causes of the patterns revealed (on which we expect much fruitful discussion elsewhere). Rather, the JRS Editorial Board aims to make key data available both to inform a much wider debate within the profession as a whole and, importantly, to inform this Journal’s policies, procedures and active outreach. The Board is also acutely aware that any analysis of gender bias needs to be framed carefully — both by an awareness that there are other under-represented groups in the discipline (on which our data in their current form would regrettably only offer a most imperfect picture), and by a sensitivity to the limitations of a conception of gender as a simple binary.

TRAC at 30: A Bibliometric Analysis of the TRAC Community

“…This would allow researchers to better analyse data citation numbers and networks to identify the theoretical developments of the organisation over the last 30 years. In addition, we could use this data, in conjunction with a survey of TRAC's past authorship, to analyse and address some of the systemic biases seen in other organisations (Eckardt 2019;Kelly et al 2019).…”

Increasing International Perspectives in Theoretical Roman Archaeology

“…At Oxford 2010, 32 of 49 speakers were female (Mladenović and Russell 2011). It is also worth noting that these figures are based on first names (a method also used in -Eckardt 2019) and so this is an imperfect analysis due to missing data on non-binary researchers and other under-represented groups (Kelly et al 2019).…”