September 2024 – DigitalKoans

Discovery Librarian at University of California Berkeley

In partnership with and under the guidance of the Head of Systems and Discovery Services, the Discovery Librarian will collaborate with stakeholders across the Library to ensure seamless access to the Library’s collections for all users to support research and teaching and to advance the University Libraries’ commitment to equity and inclusion. This might include identifying pain points and contributing to the development of user-centered access and discovery practices, strategies, and services. The Discovery Librarian will oversee configurations related to Discovery in Alma, troubleshoot issues reported by users and staff and identify solutions and/or communication opportunities, review ongoing monthly release notes, quarterly feature releases, and annual product roadmaps for Primo VE.

https://tinyurl.com/48fu5sxz

"The AI Copyright Hype: Legal Claims That Didn’t Hold Up"

Over the past year, two dozen AI-related lawsuits and their myriad infringement claims have been winding their way through the court system. None have yet reached a jury trial. While we all anxiously await court rulings that can inform our future interaction with generative AI models, in the past few weeks, we are suddenly flooded by news reports with titles such as “US Artists Score Victory in Landmark AI Copyright Case,” “Artists Land a Win in Class Action Lawsuit Against A.I. Companies,” “Artists Score Major Win in Copyright Case Against AI Art Generators”—and the list goes on. The exuberant mood in these headlines mirror the enthusiasm of people actually involved in this particular case (Andersen v. Stability AI). The plaintiffs’ lawyer calls the court’s decision “a significant step forward for the case.” “We won BIG,” writes the plaintiff on X.

In this blog post, we’ll explore the reality behind these headlines and statements. The “BIG” win in fact describes a portion of the plaintiffs’ claims surviving a pretrial motion to dismiss. If you are already familiar with the motion to dismiss per Federal Rules of Civil Procedure Rule 12(b)(6), please refer to Part II to find out what types of claims have been dismissed early on in the AI lawsuits.

https://tinyurl.com/rhmzkr8y

Digital Archivist at Old Dominion University

The Digital Archivist is responsible for creating and implementing digital preservation plans, engaging in digital curation and collection development, supporting the Libraries’ digital collections platform(s), creating and providing guidance on metadata for digital materials, and providing access to Old Dominion University’s digital collections. The Digital Archivist administers and prioritizes multiple projects and tasks to meet the goals of the Digital Collections Program, including training, and supervising student employees and interns to assist with program activities.

https://jobs.odu.edu/postings/21423

"Impact Factor Does Not Predict Long-Term Article Impact across 15 Journals"

Authors who publish in journals with higher impact factors are deemed to contribute more to their discipline. However, the impact factor of a journal does not indicate how long a specific article stays in the scientific discourse, and metrics that measure the length of time articles within a journal continue to be cited are not typically used. We examined citations of 443,732 research articles [786,064 total] between 1980 and 2020 across 15 journals. We explored the range of longevity values found across different journals as well as the relationship between impact factor and longevity. We found no relationship between impact factor and longevity, indicating that immediate attention to an article is not correlated with longer-term impact. . . .

For early-career scholars, the implications of citation longevity can be meaningful. Our data suggest that a new faculty member publishing primarily in strong society journals has yet to reach their full impact by mid-career milestones such as applying for tenure and promotion. The total contribution of the work to the field will likely not be seen until after their career is finished. . . .

The results presented here have important implications for journal selection and evaluation of science academics. For example, early career researchers may benefit from publishing in lower-impact, higher-longevity journals because their work may become classic within their field when they reach full promotion. Additionally, hiring and promotion committees should consider giving journals with higher longevity scores more weight among early career researchers, as these works can potentially impact departmental rankings over the long run. Furthermore, funding agencies and university review committees could benefit from a holistic analysis of academic productivity by examining article and journal performance metrics over time along with traditional indicators, such as altmetrics (Fortin et al., 2021), impact factor, and total citations.

https://doi.org/10.1016/j.dim.2024.100079

Manager of AI Modeling & Inference at Stanford University

Stanford University Libraries’ Research Data Services is seeking an experienced, technically-adept, forward-thinking library professional to both lead and directly contribute programming effort to our new AI Modeling & Inference group. This role manages two Digital Scholarship Research Developers, leading a group with significant accomplishments in digital humanities projects. . . .

This role is a member of the management team in Research Data Services, a patron-facing group at Stanford University Libraries supporting geospatial research data, research data curation, data infrastructure, and academic data support. Due to the relevance of modeling & inference across many domains, we expect this position to play a crucial role in articulating AI research methods across other parts of RDS. Examples of this might include text recognition on historic maps, vector-space models for reconciling text in a curation context, or re-training Large Language Models on specific historic or literary corpora.

https://tinyurl.com/58uzepc7

"Is Open Access Disrupting the Journal Business? A Perspective from Comparing Full Adopters, Partial Adopters, and Non-adopters"

This study employs the concept of disruptive innovation to develop a more systematic perspective on the impact of OA. It compares the market power of full-OA adopters with that of partial adopters and non-adopters. Using Lerner’s definition of market power, a series of mean difference tests and regressions were conducted using Lerner’s definition of market power. The findings reveal that both full-OA adopters and partial adopters exhibit greater market power than non-adopters. However, full adopters do not have more market power than partial adopters, even when compared to the subscription options of hybrid journals. This suggests that OA disrupts the market power of both incumbents and traditional businesses. Nevertheless, the situation changes once incumbents integrate an OA option into their publishing repertoire and transition to a hybrid model.

https://doi.org/10.1016/j.joi.2024.101574