A framework for assessing the peer review duration of journals: case study in computer science
Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bilalli, Besim [verfasserIn] Munir, Rana Faisal [verfasserIn] Abelló, Alberto [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978, 126(2020), 1 vom: 05. Nov., Seite 545-563 |
|---|---|
| Übergeordnetes Werk: |
volume:126 ; year:2020 ; number:1 ; day:05 ; month:11 ; pages:545-563 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11192-020-03742-9 |
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| Katalog-ID: |
SPR042801931 |
|---|
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| 520 | |a Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). | ||
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| 700 | 1 | |a Munir, Rana Faisal |e verfasserin |4 aut | |
| 700 | 1 | |a Abelló, Alberto |e verfasserin |4 aut | |
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10.1007/s11192-020-03742-9 doi (DE-627)SPR042801931 (DE-599)SPRs11192-020-03742-9-e (SPR)s11192-020-03742-9-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Bilalli, Besim verfasserin aut A framework for assessing the peer review duration of journals: case study in computer science 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). Peer review process (dpeaa)DE-He213 Review process duration (dpeaa)DE-He213 Review process quality (dpeaa)DE-He213 Munir, Rana Faisal verfasserin aut Abelló, Alberto verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2020), 1 vom: 05. Nov., Seite 545-563 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2020 number:1 day:05 month:11 pages:545-563 https://dx.doi.org/10.1007/s11192-020-03742-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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 31.00 ASE AR 126 2020 1 05 11 545-563 |
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10.1007/s11192-020-03742-9 doi (DE-627)SPR042801931 (DE-599)SPRs11192-020-03742-9-e (SPR)s11192-020-03742-9-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Bilalli, Besim verfasserin aut A framework for assessing the peer review duration of journals: case study in computer science 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). Peer review process (dpeaa)DE-He213 Review process duration (dpeaa)DE-He213 Review process quality (dpeaa)DE-He213 Munir, Rana Faisal verfasserin aut Abelló, Alberto verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2020), 1 vom: 05. Nov., Seite 545-563 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2020 number:1 day:05 month:11 pages:545-563 https://dx.doi.org/10.1007/s11192-020-03742-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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 31.00 ASE AR 126 2020 1 05 11 545-563 |
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10.1007/s11192-020-03742-9 doi (DE-627)SPR042801931 (DE-599)SPRs11192-020-03742-9-e (SPR)s11192-020-03742-9-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Bilalli, Besim verfasserin aut A framework for assessing the peer review duration of journals: case study in computer science 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). Peer review process (dpeaa)DE-He213 Review process duration (dpeaa)DE-He213 Review process quality (dpeaa)DE-He213 Munir, Rana Faisal verfasserin aut Abelló, Alberto verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2020), 1 vom: 05. Nov., Seite 545-563 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2020 number:1 day:05 month:11 pages:545-563 https://dx.doi.org/10.1007/s11192-020-03742-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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 31.00 ASE AR 126 2020 1 05 11 545-563 |
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10.1007/s11192-020-03742-9 doi (DE-627)SPR042801931 (DE-599)SPRs11192-020-03742-9-e (SPR)s11192-020-03742-9-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Bilalli, Besim verfasserin aut A framework for assessing the peer review duration of journals: case study in computer science 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). Peer review process (dpeaa)DE-He213 Review process duration (dpeaa)DE-He213 Review process quality (dpeaa)DE-He213 Munir, Rana Faisal verfasserin aut Abelló, Alberto verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2020), 1 vom: 05. Nov., Seite 545-563 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2020 number:1 day:05 month:11 pages:545-563 https://dx.doi.org/10.1007/s11192-020-03742-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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 31.00 ASE AR 126 2020 1 05 11 545-563 |
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10.1007/s11192-020-03742-9 doi (DE-627)SPR042801931 (DE-599)SPRs11192-020-03742-9-e (SPR)s11192-020-03742-9-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Bilalli, Besim verfasserin aut A framework for assessing the peer review duration of journals: case study in computer science 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). Peer review process (dpeaa)DE-He213 Review process duration (dpeaa)DE-He213 Review process quality (dpeaa)DE-He213 Munir, Rana Faisal verfasserin aut Abelló, Alberto verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2020), 1 vom: 05. Nov., Seite 545-563 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2020 number:1 day:05 month:11 pages:545-563 https://dx.doi.org/10.1007/s11192-020-03742-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_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 31.00 ASE AR 126 2020 1 05 11 545-563 |
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This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Peer review process</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Review process duration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Review process quality</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Munir, Rana Faisal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abelló, Alberto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Scientometrics</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978</subfield><subfield code="g">126(2020), 1 vom: 05. 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framework for assessing the peer review duration of journals: case study in computer science |
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A framework for assessing the peer review duration of journals: case study in computer science |
| abstract |
Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). |
| abstractGer |
Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). |
| abstract_unstemmed |
Abstract In various fields, scientific article publication is a measure of productivity and in many occasions it is used as a critical factor for evaluating researchers. Therefore, a lot of time is dedicated to writing articles that are then submitted for publication in journals. Nevertheless, the publication process in general and the review process in particular tend to be rather slow. This is the case for instance of computer science (CS) journals. Moreover, the process typically lacks in transparency, where information about the duration of the review process is at best provided in an aggregated manner, if made available at all. In this paper, we develop a framework as a step towards bringing more reliable data with respect to review duration. Based on this framework, we implement a tool—journal response time (JRT), that allows for automatically extracting the review process data and helps researchers to find the average response times of journals, which can be used to study the duration of CS journals’ peer review process. The information is extracted as metadata from the published articles, when available. This study reveals that the response times publicly provided by publishers differ from the actual values obtained by JRT (e.g., for ten selected journals the average duration reported by publishers deviates by more than 500% from the actual average value calculated from the data inside the articles), which we suspect could be from the fact that, when calculating the aggregated values, publishers consider the review time of rejected articles too (including quick desk-rejections that do not require reviewers). |
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| title_short |
A framework for assessing the peer review duration of journals: case study in computer science |
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https://dx.doi.org/10.1007/s11192-020-03742-9 |
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| author2 |
Munir, Rana Faisal Abelló, Alberto |
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Munir, Rana Faisal Abelló, Alberto |
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| doi_str |
10.1007/s11192-020-03742-9 |
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2024-07-03T14:54:54.979Z |
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| score |
7.401311 |