According to data provided by the Association of College & Research Libraries, there was a 20% decline in total academic library staffing between 2012 and 2022. Were these loses evenly distributed across areas of responsibility and sectors of higher education institutions? Despite some contradictory evidence, data shows a decline in FTE librarian positions at every type of institution except research universities. Despite a 9.6% decrease in librarian titles, there are increasing numbers of non-librarian professionals employed in academic libraries. The trend is consistent within the SUNY system, although to a lesser extent, with 291 librarians represented by the United University Professions union in 2022, down from 317 in 2011. There is also a decline in the proportion of librarians at the higher ranks of associate and full librarian within SUNY, and a three-fold increase in the number of visiting (contingent) positions. And, the number of masters degrees in library science declined over a similar period. While it is clear that librarianship within higher education is in a period of contraction, further research is needed to clarify, explicate, and understand the trend. Librarians need to understand not only what is happening within the profession, but the changing higher education landscape more generally, in order to respond most effectively in their institutional context.

We describe the development of web-based software that facilitates large-scale, crowdsourced image extraction and annotation within image-heavy corpora that are of interest to the digital humanities. An application of this software is then detailed and evaluated through a case study where it was deployed within Amazon Mechanical Turk to extract and annotate faces from the archives of Time magazine. Annotation labels included categories such as age, gender, and race that were subsequently used to train machine learning models. The systemization of our crowdsourced data collection and worker quality verification procedures are detailed within this case study. We outline a data verification methodology that used validation images and required only two annotations per image to produce high-fidelity data that has comparable results to methods using five annotations per image. Finally, we provide instructions for customizing our software to meet the needs for other studies, with the goal of offering this resource to researchers undertaking the analysis of objects within other image-heavy archives.

We extracted 327,322 faces from an archive of Time magazine containing 3,389 issues dating from 1923 to 2014, classified the gender of each extracted face, and discovered that the proportion of female faces contained within this archive varied in interesting ways over time. The proportion of female faces first peaked in the mid-to-late 1940s. This was followed by a dip lasting from the mid-1950s to the early 1960s. The 1970s saw another peak followed by a dip over the course of the 1980s. Finally, we see a slow and steady rise in the proportion of female faces from the early 1990s onwards. In this paper, we seek to make sense of these variations through an interdisciplinary framework drawing on psychology, visual studies (in particular, photography theory), and history. Through a close reading of our Time archive from the 1940s through the 1990s, we conclude that the visual representation of women in Time magazine correlates with attitudes toward women in both the historical context of the era and the textual content of the magazine.