The Ginger (Law) Librarian

The winners in the Open, New Member, and Student Divisions will receive $650 , and the Short Form Division winner will receive $300 , all generously donated by LexisNexis. Co-authors of winning papers share awards. Recipients are recognized during award ceremonies at the AALL Annual Meeting and will be given the opportunity to present their papers in a program. See the Call for Papers website !

In early March, the University of California , one of the largest research institutions in the world, blew up negotiations with Elsevier, one of the largest publishers of research articles in the world. The university would no longer pay Elsevier millions of dollars a year to subscribe to its journals. It simply walked away. Despite months of contract negotiations , Elsevier was unwilling to meet UC’s key goal: securing universal open access to UC research while containing the rapidly escalating costs associated with for-profit journals. UC's goal of open access is something that every institution should move toward because: (1) At the same time academic institutions are paying for access to journals, their employees are providing labor to journals for free. AND (2) journals pay for the research that they publish. In the United States, research funding often comes from government agencies—in other words, from taxpayers. Yet if members of the public tried to read new acad...

In spring 2017, I briefly discussed the issues  with scholarship impact factor in law as a response to a recommendation by a law professor to create a rankings methodology based on Google Scholar citation. Now US News is trying to get in the game of creating a ranking of law faculty by scholarship impact factor using Hein publication metrics. US News is asking each law school for the names and other details of its fall 2018 full-time tenured and tenure-track faculty. US News plans to link the names of each individual law school's faculty to citations and publications that were published in the previous five years and are available in HeinOnline. Using this data, HeinOnline will compile faculty scholarly impact indicators for each law school . This will include such measures as mean citations per faculty member, median citations per faculty member, and total number of publications. Those measures will then be provided to US News for use in eventually creating a comprehensiv...

As we are increasingly aware, the ethical Duty of Technology Competence requires lawyers to keep abreast of “changes in the law and its practice, including the benefits and risks associated with relevant technology. ” To date, 35 states have adopted the duty . In a previous post , I highlighted  the risks of blindly relying on algorithmic results  (relevant technology) as a potential violation of the Duty of Technology Competence. We now have case law from Canada focusing on the benefits of using algorithmic results to perform legal research. In fact, this case law may be interpreted as requiring the use of algorithmic results when ethically performing legal research.  In both Cass v. 1410088 Ontario Inc. (“Cass”) and Drummond v. The Cadillac Fairview Corp. Ltd. (“Drummond”) justices of the Ontario Superior Court made comments about artificial intelligence and legal research. The Cass case was a slip and fall in which the defendant prevailed. The plaintiff, who ...

If you're into algorithms, you should absolutely subscribe to the MIT Technology Review newsletter called The Algorithm . Earlier this week, the folks at The Algorithm asked "what is AI, exactly?" The answer is reproduced below. The question may seem basic, but the answer is kind of complicated. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can. As it currently stands, the vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning. These algorithms use statistics to find patterns in massive amounts of data. They then use those patterns to make predictions on things like what shows you might like on Netflix, what you’re saying when you speak to Alexa, or whether you have cancer based on your MRI. Machine learning, and its subset deep learning...

As a member of the AALL State of the Profession Survey Advisory Group , I am excited that the survey has been released! The Advisory Group is comprised of librarians from all types of law libraries with the purpose of designing a survey to assess the current state of the profession. The State of the Profession Survey will document the current landscape of law libraries, specific to each library type, and will provide benchmarking in the following areas: Technology, collections and library resources, constituent services, institutional outcomes, research competencies, training, staffing, and leadership. The purpose of the State of the Profession Survey is to provide members and their organizations with the information and insights they need to effectively assess, advocate, and strategically prepare for the future. We started working on the survey in 2017 with this purpose in mind. In the survey, you will find questions pertaining to the various enumerated areas. While the sur...

At the Ohio Regional Association of Law Libraries (ORALL) Annual Meeting, as I presented on the  duty of technology competence in the algorithmic society , an astute law librarian asked (paraphrasing), "how does fake news play into this?" That question gave rise to a flurry of brain activity, as I considered how Google, for example, ranks relevancy, the rise of fake news, and the ability of users to spot fake news sources -- particularly for legal research. As I was presenting to a group of lawyers at a CLE this week, I polled them asking about the electronic resource that they primarily use for legal research. The overwhelming response was Google. Google uses a trademarked, proprietary – mostly secret – algorithm called PageRank, which assigns each webpage a relevancy score based on factors, such as: The frequency and location of keywords within the webpage. If the keyword only appears once within the body of the page, it will receive a low score for that key...