Searcharxiv⌕ Search

arXiv subjects

Andrea Corvillón

Publications and source records attributed to Andrea Corvillón.

3 recordsLinked to original sources

Distributed Peer Review at ALMA: An Empirical Comparison with Panel-Based Review

Large astronomical observatories are increasingly adopting distributed peer review (DPR) to manage growing proposal volumes, yet empirical comparisons with panel-based systems remain limited. Beginning in 2021 (Cycle 8), the Atacama Large Millimeter/submillimeter Array (ALMA) transitioned to DPR for the majority of proposals, with DPR applied to nearly all proposals from Cycle 9 onward. Under DPR, each Principal Investigator (PI) designates a reviewer to evaluate a set of proposals without collective discussion. This study analyzes over 20,000 proposals and 160,000 reviews spanning 13 cycles to assess the impact of this change. We find that DPR largely reproduces the systematic ranking trends observed in panel evaluations across PI demographics, technical characteristics, and scientific areas, consistent with panel outcomes both before and after discussion. Scientific diversity among the top-ranked proposals is similar between DPR and post-discussion panel rankings. Individual proposal ranks show substantial dispersion under both DPR and panel assessments prior to discussion, with discussion only partially reducing this variance. The observed dispersion therefore reflects intrinsic variation in reviewer judgments rather than a byproduct of the distributed process itself. In Cycle 12, reviewers rated the majority of DPR written comments as high or adequate quality, with no dependence on reviewer career stage. However, 10% of reviews were rated as low quality, highlighting the challenge of maintaining quality standards across the approximately 16,000 reviews produced each cycle. Overall, our results indicate that DPR reproduces the population-level ranking structure obtained in panel review, despite differences in review mechanics and the role of discussion.

astro-ph.IM↗

Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA

The increasing volume of papers and proposals that undergo peer review emphasizes the pressing need for greater automation to effectively manage the growing scale. In this study, we present the deployment and evaluation of machine learning and optimization techniques to assign proposals to reviewers that were developed for the Atacama Large Millimeter/submillimeter Array (ALMA) during the Cycle 10 Call for Proposals issued in 2023. Using topic modeling algorithms, we identify the proposal topics and assess reviewers' expertise based on their previous ALMA proposal submissions. We then apply an adapted version of the assignment optimization algorithm from PeerReview4All (Stelmakh et al. 2021) to maximize the alignment between proposal topics and reviewer expertise. Our evaluation shows a significant improvement in matching reviewer expertise: the median similarity score between the proposal topic and reviewer expertise increased by 51 percentage points compared to the previous cycle, and the percentage of reviewers reporting expertise in their assigned proposals rose by 20 percentage points. Furthermore, the assignment process proved highly effective in that no proposals required reassignment due to significant mismatches, resulting in a savings of 3 to 5 days of manual effort.

astro-ph.IM↗

Analysis of the ALMA Cycle 8 Distributed Peer Review Process

In response to the challenges presented by high reviewer workloads in traditional panel reviews and increasing numbers of submitted proposals, ALMA implemented distributed peer review to assess the majority of proposals submitted to the Cycle 8 Main Call. In this paper, we present an analysis of this review process. Over 1000 reviewers participated in the process to review 1497 proposals, making it the largest implementation of distributed peer review to date in astronomy, and marking the first time this process has been used to award the majority of observing time at an observatory. We describe the process to assign proposals to reviewers, analyze the nearly 15,000 ranks and comments submitted by reviewers to identify any trends and systematics, and gather feedback on the process from reviewers and Principal Investigators (PIs) through surveys. Approximately 90% of the proposal assignments were aligned with the expertise of the reviewer, as measured both by the expertise keywords provided by the reviewers and the reviewers' self-assessment of their expertise on their assigned proposals. PIs rated 73% of the individual review comments as helpful, and even though the reviewers had a broad range of experience levels, PIs rated the quality of the comments received from students and senior researchers similarly. The primary concerns raised by PIs were the quality of some reviewer comments and high dispersions in the ranks. The ranks and comments are correlated with various demographics to identify the main areas in which the review process can be improved in future cycles.

astro-ph.IM↗