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Special collections and archives workers have unique experiences in providing service to their patron base while also working to overcome disruption and physical displacement. The disruption may take the form of a major renovation, a closure due to natural disaster, being forced out of a library space, budget cuts, or other operational issues. This article captures the experiences of twenty-eight staff members providing access services to their subset of users, in the form of interviews, in order to reflect upon how our profession connects with, provides services to, and places value on the relationships we cultivate with the public. We sought to gain insight from those who have experienced disruptions to their services in the past in order to apply this knowledge to the pandemic situation of 2020 and 2021, as well as those experiencing major disruptions in the future. The interview responses are a general representation of what staff may endure when embarking on a project that is a major disruption to service delivery. The experiences of the participants revealed a cyclical interconnectedness between the themes, indicating the complex nature of work involving human beings. The responses provide tangible recommendations to those planning for, beginning, or experiencing a disruption. [ABSTRACT FROM AUTHOR]

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Although a great deal of attention has been paid to how conspiracy theories circulate on social media, and the deleterious effect that they, and their factual counterpart conspiracies, have on political institutions, there has been little computational work done on describing their narrative structures. Predicating our work on narrative theory, we present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories that circulate on social media, and actual conspiracies reported in the news media. We base this work on two separate comprehensive repositories of blog posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey political conspiracy Bridgegate from 2013. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative machine learning model where nodes represent actors/actants, and multi-edges and self-loops among nodes capture context-specific relationships. Posts and news items are viewed as samples of subgraphs of the hidden narrative framework network. The problem of reconstructing the underlying narrative structure is then posed as a latent model estimation problem. To derive the narrative frameworks in our target corpora, we automatically extract and aggregate the actants (people, places, objects) and their relationships from the posts and articles. We capture context specific actants and interactant relationships by developing a system of supernodes and subnodes. We use these to construct an actant-relationship network, which constitutes the underlying generative narrative framework for each of the corpora. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction, and hypothesize that this multi-domain focus is an important feature of conspiracy theories. We contrast this to the single domain focus of an actual conspiracy. While Pizzagate relies on the alignment of multiple domains, Bridgegate remains firmly rooted in the single domain of New Jersey politics. We hypothesize that the narrative framework of a conspiracy theory might stabilize quickly in contrast to the narrative framework of an actual conspiracy, which might develop more slowly as revelations come to light. By highlighting the structural differences between the two narrative frameworks, our approach could be used by private and public analysts to help distinguish between conspiracy theories and conspiracies. [ABSTRACT FROM AUTHOR]

During the Fall 2017 semester, staff at the Hudson County Community College (HCCC), Library began to notice that many books listed as available in the catalog were often not being found on the shelves when patrons attempted to retrieve them. This situation puzzled library leadership because HCCC had recently conducted an inventory and removed all missing items from its holdings. To determine the cause of this discrepancy, HCCC staff decided to sample the library's collection to determine if books were available at the expected locations. From this, the library found that a high percentage of its books were not present where they were expected to be. In response, library staff implemented a variety of changes to HCCC's shelving and access services operations.

The authors investigated quantitative methods of collection use analysis employing library data that are available in ILS and ILL systems to better understand library collection use and user needs. For the purpose of the study, the authors extracted circulation and ILL records from the library's systems using data-mining techniques. By comparing these data to records of books acquired in a four-year period, the study reveals generally good collection use as well as some unmet collection needs. The study also offers a method of compiling and sharing these multiple types of usage data among work groups to create feedback mechanisms to inform the work of selectors and suggest modifications to collection development practices.