Author
Francisco Grimaldo
Bio: Francisco Grimaldo is an academic researcher from University of Valencia. The author has contributed to research in topics: Peer review & Similarity learning. The author has an hindex of 14, co-authored 80 publications receiving 730 citations.
Papers published on a yearly basis
Papers
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TL;DR: It is found that publishing reports did not significantly compromise referees’ willingness to review, recommendations, or turn-around times, and suggest that open peer review does not compromise the process, at least when referees are able to protect their anonymity.
Abstract: To increase transparency in science, some scholarly journals are publishing peer review reports. But it is unclear how this practice affects the peer review process. Here, we examine the effect of ...
177 citations
TL;DR: The authors found that during the early months of the COVID-19 pandemic, there was an unusually high submission rate of scholarly articles, which may have penalized the scientific productivity of women.
Abstract: During the early months of the COVID-19 pandemic, there was an unusually high submission rate of scholarly articles. Given that most academics were forced to work from home, the competing demands for familial duties may have penalized the scientific productivity of women. To test this hypothesis, we looked at submitted manuscripts and peer review activities for all Elsevier journals between February and May 2018-2020, including data on over 5 million authors and referees. Results showed that during the first wave of the pandemic, women submitted proportionally fewer manuscripts than men. This deficit was especially pronounced among more junior cohorts of women academics. The rate of the peer-review invitation acceptance showed a less pronounced gender pattern with women taking on a greater service responsibility for journals, except for health & medicine, the field where the impact of COVID-19 research has been more prominent. Our findings suggest that the first wave of the pandemic has created potentially cumulative advantages for men.
86 citations
TL;DR: This article examined gender bias in peer review with data for 145 journals in various fields of research, including about 1.7 million authors and 740,000 referees, and found that manuscripts written by women as solo authors or coauthored by women were treated even more favorably by referees and editors.
Abstract: Scholarly journals are often blamed for a gender gap in publication rates, but it is unclear whether peer review and editorial processes contribute to it. This article examines gender bias in peer review with data for 145 journals in various fields of research, including about 1.7 million authors and 740,000 referees. We reconstructed three possible sources of bias, i.e., the editorial selection of referees, referee recommendations, and editorial decisions, and examined all their possible relationships. Results showed that manuscripts written by women as solo authors or coauthored by women were treated even more favorably by referees and editors. Although there were some differences between fields of research, our findings suggest that peer review and editorial processes do not penalize manuscripts by women. However, increasing gender diversity in editorial teams and referee pools could help journals inform potential authors about their attention to these factors and so stimulate participation by women.
69 citations
TL;DR: The authors found that during the first wave of the COVID-19 pandemic, women submitted proportionally fewer manuscripts than men and this imbalance was especially pronounced among younger cohorts of women academics.
Abstract: During the early months of the COVID-19 pandemic, the submission rate to scholarly journals increased abnormally. Given that most academics were forced to work from home, the competing demands for familial duties might have penalised the scientific productivity of women. To test this hypothesis, we looked at submitted manuscripts and peer review activities for all Elsevier journals between February and May 2018-2020, including data on over 5 million authors and referees. Results showed that during the first wave of the pandemic, women submitted proportionally fewer manuscripts than men. This deficit was especially pronounced among younger cohorts of women academics. The rate of the peer-review invitation acceptance showed a less pronounced gender pattern. Our findings suggest that the first wave of the pandemic has created potentially cumulative advantages for men.
65 citations
01 Jan 2010
TL;DR: This paper has compared a partitioning method based on convex hulls with two techniques that use rectangular regions and shows that the shape of the regions in the partition can improve the performance of the partitions method, rather than the heuristic method used.
Abstract: The simulation of large crowds of autonomous agents with realistic behavior is still a challenge for several computer research communities. In order to handle large crowds, some scalable architectures have been proposed. Nevertheless, the effective use of distributed systems requires the use of partitioning methods that can properly distribute the workload generated by agents among the existing distributed resources. In this paper, we analyze the use of irregular shape regions (convex hulls) for solving the partitioning problem. We have compared a partitioning method based on convex hulls with two techniques that use rectangular regions. The performance evaluation results show that the convex hull method outperforms the rest of the considered methods in terms of both fitness function values and execution times, regardless of the movement pattern followed by the agents. These results show that the shape of the regions in the partition can improve the performance of the partitioning method, rather than the heuristic method used.
62 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
Journal Article•
9,185 citations
01 Jan 1995
TL;DR: In this paper, the authors propose a method to improve the quality of the data collected by the data collection system. But it is difficult to implement and time consuming and computationally expensive.
Abstract: 本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。
1,636 citations
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TL;DR: The Social Contexts of Research as mentioned in this paper is a collection of articles about the social context of research in the 1970s and 1980s, edited by Saad Z. Nagi and Ronald G. Corwin. Pp. xii + 409.
Abstract: The Social Contexts of Research. Edited by Saad Z. Nagi and Ronald G. Corwin. Pp. xii + 409. (John Wiley: New York and London, August 1972.) £5.65.
1,206 citations