Month: September 2024

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

| Digital Library Jobs |
| Electronic Resources Jobs |
| Library IT Jobs |
| Digital Scholarship |

"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

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |