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1. Carol Upadhya and Sachinkumar Rathod, Caste at the City’s Edge: Land Struggles in Peri-urban Bangalore. 26: 1-28.

   Upadhya, Carol; Rathod, Sachinkumar (April 2021, South Asia multidisciplinary academic journal)

   null (Ed.)

   This paper draws on fieldwork carried out between 2017 and 2020 as part of the project “Speculative Urbanism: Land, Livelihoods, and Finance Capital,” in collaboration with the University of Minnesota, funded by the National Science Foundation [grant number BCS-1636437]. We thank our colleagues Vinay Gidwani, Michael Goldman, Hemangini Gupta, Eric Sheppard, Helga Leitner, and other members of the research team for many discussions and debates. Earlier versions of the paper were presented at the Third Annual Research Conference of the Indian Institute for Human Settlements, Bengaluru, January 10–12, 2019; in the panel on The Peri-urban Question: Renewing Concepts and Categories at RC21@Delhi, September 18–20, 2019; and at the French Institute of Pondicherry, February 13, 2020. We are grateful to the organizers and participants of those conferences for their valuable feedback, as well as to the anonymous referees for their constructive comments. All photos are by Pierre Hauser, who we sincerely thank for allowing us to use his work. 

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2. On the Detection of Reviewer-Author Collusion Rings From Paper Bidding

   Jecmen, Steven; Shah, Nihar; Fang, Fei; Akoglu, Leman (December 2024, Transactions on machine learning research)

   Collusion rings pose a significant threat to peer review. In these rings, reviewers who are also authors coordinate to manipulate paper assignments, often by strategically bidding on each other’s papers. A promising solution is to detect collusion through these manipulated bids, enabling conferences to take appropriate action. However, while methods exist for detecting other types of fraud, no research has yet shown that identifying collusion rings is feasible. In this work, we consider the question of whether it is feasible to detect collusion rings from the paper bidding. We conduct an empirical analysis of two realistic conference bidding datasets and evaluate existing algorithms for fraud detection in other applications. We find that collusion rings can achieve considerable success at manipulating the paper assignment while remaining hidden from detection: for example, in one dataset, undetected colluders are able to achieve assignment to up to 30% of the papers authored by other colluders. In addition, when 10 colluders bid on all of each other’s papers, no detection algorithm outputs a group of reviewers with more than 31% overlap with the true colluders. These results suggest that collusion cannot be effectively detected from the bidding using popular existing tools, demonstrating the need to develop more complex detection algorithms as well as those that leverage additional metadata (e.g., reviewer-paper text-similarity scores). 

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3. Using Artificial Intelligence to Support Peer-to-Peer Discussions in Science Classrooms

   https://doi.org/10.3390/educsci14121411  

   Billings, Kelly; Chang, Hsin-Yi; Lim-Breitbart, Jonathan M; Linn, Marcia C (December 2024, Education Sciences)

   In successful peer discussions students respond to each other and benefit from supports that focus discussion on one another’s ideas. We explore using artificial intelligence (AI) to form groups and guide peer discussion for grade 7 students. We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups. We embedded the chat tool in an earth science unit and tested it in two classrooms at the same school. We report on the accuracy of the NLP idea detection, the impact of maximized versus random grouping, and the role of the question bank in focusing the discussion on student ideas. We found that the similarity of student ideas limited the value of maximizing idea variety and that the question bank facilitated students’ use of knowledge integration processes. 

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4. Conversations and deliberations: Non-standard cosmological epochs and expansion histories

   https://doi.org/10.1142/S0217751X25300042  

   Batell, Brian; Dienes, Keith R; Thomas, Brooks; Watson, Scott; Allahverdi, Rouzbeh; Amin, Mustafa; Boddy, Kimberly K; Delos, M Sten; Erickcek, Adrienne L; Ghalsasi, Akshay; et al (June 2025, International Journal of Modern Physics A)

   This paper summarizes the discussions which took place during the PITT-PACC Workshop entitled “Non-Standard Cosmological Epochs and Expansion Histories,” held in Pittsburgh, Pennsylvania, Sept. 5–7, 2024. Much like the non-standard cosmological epochs that were the subject of these discussions, the format of this workshop was also non-standard. Rather than consisting of a series of talks from participants, with each person presenting their own work, this workshop was instead organized around free-form discussion blocks, with each centered on a different overall theme and guided by a different set of Discussion Leaders. This document is not intended to serve as a comprehensive review of these topics, but rather as an informal record of the discussions that took place during the workshop, in the hope that the content and free-flowing spirit of these discussions may inspire new ideas and research directions. 

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5. Design and analysis of the nips 2016 review process

   Shah, Nihar B; Tabibian, Behzad; Muandet, Krikamol; Guyon, Isabelle; Von Luxburg, Ulrike (January 2018, Journal of machine learning research)

   Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100% in terms of attendees as compared to the previous year. The massive scale as well as rapid growth of the conference calls for a thorough quality assessment of the peer-review process and novel means of improvement. In this paper, we analyze several aspects of the data collected during the review process, including an experiment investigating the efficacy of collecting ordinal rankings from reviewers. We make a number of key observations, provide suggestions that may be useful for subsequent conferences, and discuss open problems towards the goal of improving peer review. 

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