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Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
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- Bibliometric analysis (1)
- Contrastive learning (1)
- Deep learning (1)
- Discrete event simulation (1)
- Future of simulation (1)
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- Link relation types (1)
- Link sets (1)
- Open science practices (1)
- Origin (1)
- Questionable research practices (1)
- Replicability (1)
- Reproducibility (1)
- Scientific claim extraction (1)
- Scientific misconduct (1)
- Serialization (1)
- Signposting (1)
- Transfer learning (1)
- Trustworthiness of our scientific knowledge (1)
- Typed links (1)
- Word embedding (1)
Articles 1 - 4 of 4
Full-Text Articles in Social and Behavioral Sciences
Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson
Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson
Computer Science Faculty Publications
[First paragraph] This page details concrete recipes that platforms that host research outputs (e.g. data repositories, institutional repositories, publisher platforms, etc.) can follow to implement Signposting, a lightweight yet powerful approach to increase the FAIRness of scholarly objects.
Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li
Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li
Computer Science Faculty Publications
The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting …
The Trustworthiness Of The Cumulative Knowledge In Industrial/Organizational Psychology: The Current State Of Affairs And A Path Forward, Sheila K. Keener, Sven Kepes, Ann-Kathrin Torka
The Trustworthiness Of The Cumulative Knowledge In Industrial/Organizational Psychology: The Current State Of Affairs And A Path Forward, Sheila K. Keener, Sven Kepes, Ann-Kathrin Torka
Management Faculty Publications
The goal of industrial/organizational (IO) psychology, is to build and organize trustworthy knowledge about people-related phenomena in the workplace. Unfortunately, as with other scientific disciplines, our discipline may be experiencing a “crisis of confidence” stemming from the lack of reproducibility and replicability of many of our field's research findings, which would suggest that much of our research may be untrustworthy. If a scientific discipline's research is deemed untrustworthy, it can have dire consequences, including the withdraw of funding for future research. In this focal article, we review the current state of reproducibility and replicability in IO psychology and related fields. …
Past Challenges And The Future Of Discrete Event Simulation, Andrew J. Collins, Farinaz Sabz Ali Pour, Craig A. Jordan
Past Challenges And The Future Of Discrete Event Simulation, Andrew J. Collins, Farinaz Sabz Ali Pour, Craig A. Jordan
Engineering Management & Systems Engineering Faculty Publications
The American scientist Carl Sagan once said: “You have to know the past to understand the present.” We argue that having a meaningful dialogue on the future of simulation requires a baseline understanding of previous discussions on its future. For this paper, we conduct a review of the discrete event simulation (DES) literature that focuses on its future to understand better the path that DES has been following, both in terms of who is using simulation and what directions they think DES should take. Our review involves a qualitative literature review of DES and a quantitative bibliometric analysis of the …