Enhancing the examination of obstacles in an automated peer review system
Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what ch...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fernandes, Gustavo Lúcius [verfasserIn] Vaz-de-Melo, Pedro O. S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal on digital libraries - Springer Berlin Heidelberg, 1997, 25(2023), 2 vom: 02. Dez., Seite 341-364 |
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| Übergeordnetes Werk: |
volume:25 ; year:2023 ; number:2 ; day:02 ; month:12 ; pages:341-364 |
| Links: |
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| DOI / URN: |
10.1007/s00799-023-00382-1 |
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SPR056367368 |
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| 520 | |a Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. | ||
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10.1007/s00799-023-00382-1 doi (DE-627)SPR056367368 (SPR)s00799-023-00382-1-e DE-627 ger DE-627 rakwb eng 070 004 VZ 24,1 ssgn 05.38 bkl 54.80 bkl 06.54 bkl Fernandes, Gustavo Lúcius verfasserin (orcid)0000-0002-1748-8976 aut Enhancing the examination of obstacles in an automated peer review system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. Peer review (dpeaa)DE-He213 Hard instances (dpeaa)DE-He213 Polarity classification (dpeaa)DE-He213 Vaz-de-Melo, Pedro O. S. verfasserin (orcid)0000-0002-9749-0151 aut Enthalten in International journal on digital libraries Springer Berlin Heidelberg, 1997 25(2023), 2 vom: 02. Dez., Seite 341-364 (DE-627)253724198 (DE-600)1459210-1 1432-1300 nnns volume:25 year:2023 number:2 day:02 month:12 pages:341-364 https://dx.doi.org/10.1007/s00799-023-00382-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 05.38 VZ 54.80 VZ 06.54 VZ AR 25 2023 2 02 12 341-364 |
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10.1007/s00799-023-00382-1 doi (DE-627)SPR056367368 (SPR)s00799-023-00382-1-e DE-627 ger DE-627 rakwb eng 070 004 VZ 24,1 ssgn 05.38 bkl 54.80 bkl 06.54 bkl Fernandes, Gustavo Lúcius verfasserin (orcid)0000-0002-1748-8976 aut Enhancing the examination of obstacles in an automated peer review system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. Peer review (dpeaa)DE-He213 Hard instances (dpeaa)DE-He213 Polarity classification (dpeaa)DE-He213 Vaz-de-Melo, Pedro O. S. verfasserin (orcid)0000-0002-9749-0151 aut Enthalten in International journal on digital libraries Springer Berlin Heidelberg, 1997 25(2023), 2 vom: 02. Dez., Seite 341-364 (DE-627)253724198 (DE-600)1459210-1 1432-1300 nnns volume:25 year:2023 number:2 day:02 month:12 pages:341-364 https://dx.doi.org/10.1007/s00799-023-00382-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 05.38 VZ 54.80 VZ 06.54 VZ AR 25 2023 2 02 12 341-364 |
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10.1007/s00799-023-00382-1 doi (DE-627)SPR056367368 (SPR)s00799-023-00382-1-e DE-627 ger DE-627 rakwb eng 070 004 VZ 24,1 ssgn 05.38 bkl 54.80 bkl 06.54 bkl Fernandes, Gustavo Lúcius verfasserin (orcid)0000-0002-1748-8976 aut Enhancing the examination of obstacles in an automated peer review system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. Peer review (dpeaa)DE-He213 Hard instances (dpeaa)DE-He213 Polarity classification (dpeaa)DE-He213 Vaz-de-Melo, Pedro O. S. verfasserin (orcid)0000-0002-9749-0151 aut Enthalten in International journal on digital libraries Springer Berlin Heidelberg, 1997 25(2023), 2 vom: 02. Dez., Seite 341-364 (DE-627)253724198 (DE-600)1459210-1 1432-1300 nnns volume:25 year:2023 number:2 day:02 month:12 pages:341-364 https://dx.doi.org/10.1007/s00799-023-00382-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 05.38 VZ 54.80 VZ 06.54 VZ AR 25 2023 2 02 12 341-364 |
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10.1007/s00799-023-00382-1 doi (DE-627)SPR056367368 (SPR)s00799-023-00382-1-e DE-627 ger DE-627 rakwb eng 070 004 VZ 24,1 ssgn 05.38 bkl 54.80 bkl 06.54 bkl Fernandes, Gustavo Lúcius verfasserin (orcid)0000-0002-1748-8976 aut Enhancing the examination of obstacles in an automated peer review system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. Peer review (dpeaa)DE-He213 Hard instances (dpeaa)DE-He213 Polarity classification (dpeaa)DE-He213 Vaz-de-Melo, Pedro O. S. verfasserin (orcid)0000-0002-9749-0151 aut Enthalten in International journal on digital libraries Springer Berlin Heidelberg, 1997 25(2023), 2 vom: 02. Dez., Seite 341-364 (DE-627)253724198 (DE-600)1459210-1 1432-1300 nnns volume:25 year:2023 number:2 day:02 month:12 pages:341-364 https://dx.doi.org/10.1007/s00799-023-00382-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 05.38 VZ 54.80 VZ 06.54 VZ AR 25 2023 2 02 12 341-364 |
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10.1007/s00799-023-00382-1 doi (DE-627)SPR056367368 (SPR)s00799-023-00382-1-e DE-627 ger DE-627 rakwb eng 070 004 VZ 24,1 ssgn 05.38 bkl 54.80 bkl 06.54 bkl Fernandes, Gustavo Lúcius verfasserin (orcid)0000-0002-1748-8976 aut Enhancing the examination of obstacles in an automated peer review system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. Peer review (dpeaa)DE-He213 Hard instances (dpeaa)DE-He213 Polarity classification (dpeaa)DE-He213 Vaz-de-Melo, Pedro O. S. verfasserin (orcid)0000-0002-9749-0151 aut Enthalten in International journal on digital libraries Springer Berlin Heidelberg, 1997 25(2023), 2 vom: 02. Dez., Seite 341-364 (DE-627)253724198 (DE-600)1459210-1 1432-1300 nnns volume:25 year:2023 number:2 day:02 month:12 pages:341-364 https://dx.doi.org/10.1007/s00799-023-00382-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 05.38 VZ 54.80 VZ 06.54 VZ AR 25 2023 2 02 12 341-364 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. 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enhancing the examination of obstacles in an automated peer review system |
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Enhancing the examination of obstacles in an automated peer review system |
| abstract |
Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model’s performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Enhancing the examination of obstacles in an automated peer review system |
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| score |
7.3989124 |