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| Review on Division of Labor in Scientometrics |
| Tian Xuecan1,2,3, Chen Liyue1,2, Ding Jielan1,2 |
1.National Science Library, Chinese Academy of Sciences, Beijing 100190
2.Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
3.School of Information Management, Wuhan University, Wuhan 430072 |
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Abstract Scientific collaboration is an important mode for researchers to conduct research activities. As the foundation of scientific collaboration, the division of labor (DOL) is of significance for optimization of team organization, improvement in research efficiency, and perfection of the contribution evaluation mechanism. We present a systematic review of the research on the classification, identification, and application of the DOL in Scientometrics, providing a reference for subsequent studies. First, starting from the taxonomy of DOL, we systematically investigate and sort out the taxonomies proposed by academic organizations, academic journals, and scholars. Second, we sort out the classification methods of DOL, which are divided into two levels: manual classification methods based on small-scale data and automatic classification methods based on large-scale data. Third, we sort out the application research of DOL from three aspects: characteristic research, structural research, and utility research. Finally, we discuss the frontier trend of artificial intelligence as a new type of labor division subject. We show that the taxonomies of DOL are relatively scattered, mainly application-oriented, and lack systematic theoretical research support. The classification methods of DOL have gradually shifted from manual annotation to text mining and machine learning, but the use of large language models is relatively rare. Regarding the application research, most studies focused on feature revelation of DOL, while few were performed on the organizational model and effects of DOL. In addition, the artificial intelligence, as a new subject of DOL, has triggered a new thinking about scientific collaboration models.
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Received: 04 March 2025
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1 黄颖, 李瑞婻, 刘晓婷, 等. 科研团队学: 内涵、进展与展望[J]. 图书情报工作, 2022, 66(4): 45-55.
2 Gl?nzel W. National characteristics in international scientific co-authorship relations[J]. Scientometrics, 2001, 51(1): 69-115.
3 明均仁, 党永杰. 基于科研贡献度加权的作者合作网络对比研究[J]. 数字图书馆论坛, 2016(1): 34-40.
4 Xing Y M, Wang F H, Zeng A, et al. Solving the cold-start problem in scientific credit allocation[J]. Journal of Informetrics, 2021, 15(3): 101157.
5 Clement T P. Who are coauthors and what should be their responsibilities?[J]. Environmental Science & Technology, 2015, 49(6): 3265-3266.
6 Allen L, Scott J, Brand A, et al. Publishing: credit where credit is due[J]. Nature, 2014, 508(7496): 312-313.
7 Smith A. An inquiry into the nature and causes of the wealth of nations[M]. London: Methuen & Co., Ltd, 1776.
8 Lu C, Zhang Y Y, Ahn Y Y, et al. Co-contributorship network and division of labor in individual scientific collaborations[J]. Journal of the Association for Information Science and Technology, 2020, 71(10): 1162-1178.
9 Larivière V, Pontille D, Sugimoto C R. Investigating the division of scientific labor using the Contributor Roles Taxonomy (CRediT)[J]. Quantitative Science Studies, 2021, 2(1): 111-128.
10 Contributor role taxonomy (CRediT)[EB/OL]. [2024-10-27]. https://credit.niso.org/.
11 ICMJE. Defining the role of authors and contributors[EB/OL]. [2024-05-15]. https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html.
12 张晓林, 彭希珺. 用高水平学术规范保障论文学术质量[J]. 现代图书情报技术, 2014(1): 1-3.
13 Nature. Authorship[EB/OL]. [2024-05-15]. https://www.nature.com/nature/editorial-policies/authorship#author-contribution-statements.
14 Science. Instructions for preparing an initial manuscript[EB/OL]. [2024-05-21]. https://www.science.org/content/page/instructions-preparing-initial-manuscript.
15 张闪闪, 顾立平. 作者贡献声明政策的初探性研究[J]. 中国科技期刊研究, 2015, 26(11): 1113-1121.
16 PLoS One. Authorship[EB/OL]. [2024-05-15]. https://journals.plos.org/plosone/s/authorship.
17 丁敬达, 王新明. 作者贡献声明及与作者署名之间的关系——基于3种图情学期刊的实证研究[J]. 图书情报工作, 2017, 61(24): 63-70.
18 Osborne J W, Holland A. What is authorship, and what should it be? A survey of prominent guidelines for determining authorship in scientific publications[J]. Practical Assessment, Research & Evaluation, 2009, 14(15): Article No.15.
19 Bates T, Ani? A, Maru?i? M, et al. Authorship criteria and disclosure of contributions: comparison of 3 general medical journals with different author contribution forms[J]. JAMA, 2004, 292(1): 86-88.
20 Larivière V, Desrochers N, Macaluso B, et al. Contributorship and division of labor in knowledge production[J]. Social Studies of Science, 2016, 46(3): 417-435.
21 Vinkler P. Research contribution, authorship and team cooperativeness[J]. Scientometrics, 1993, 26(1): 213-230.
22 Cai X Y, Han T. Analysis of the division of labor in China’s high-quality life sciences research[J]. Scientometrics, 2020, 125(2): 1077-1094.
23 Bhandari M, Guyatt G H, Kulkarni A V, et al. Perceptions of authors’ contributions are influenced by both byline order and designation of corresponding author[J]. Journal of Clinical Epidemiology, 2014, 67(9): 1049-1054.
24 Perneger T V, Poncet A, Carpentier M, et al. Thinker, soldier, scribe: cross-sectional study of researchers’ roles and author order in the Annals of Internal Medicine[J]. BMJ Open, 2017, 7(6): e013898.
25 Rahman M T, Mac Regenstein J, Abu Kassim N L, et al. The need to quantify authors’ relative intellectual contributions in a multi-author paper[J]. Journal of Informetrics, 2017, 11(1): 275-281.
26 Sundling P. The many hands of science: commonalities and differences in the research contributions of authors and subauthors[J]. Aslib Journal of Information Management, 2017, 69(5): 591-606.
27 崔林蔚, 陆颖. 基于作者署名排序的作者贡献要素分析——以《图书情报工作》2015—2016年作者贡献声明为例[J]. 图书情报工作, 2017, 61(9): 80-86.
28 张闪闪. 作者贡献权重算法优化研究[D]. 北京: 中国科学院大学, 2016.
29 Herz N, Dan O, Censor N, et al. Authors overestimate their contribution to scientific work, demonstrating a strong bias[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(12): 6282-6285.
30 Clement T P. Authorship matrix: a rational approach to quantify individual contributions and responsibilities in multi-author scientific articles[J]. Science and Engineering Ethics, 2014, 20(2): 345-361.
31 Minaee S, Kalchbrenner N, Cambria E, et al. Deep learning—based text classification: a comprehensive review[J]. ACM Computing Surveys, 2021, 54(3): Article No.62.
32 Lin Y L, Frey C B, Wu L F. Remote collaboration fuses fewer breakthrough ideas[J]. Nature, 2023, 623(7989): 987-991.
33 Xu F L, Wu L F, Evans J. Flat teams drive scientific innovation[J]. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(23): e2200927119.
34 de Souza E M, Storopoli J E, Alves W A L. Scientific contribution list categories investigation: a comparison between three mainstream medical journals[J]. Scientometrics, 2022, 127(5): 2249-2276.
35 Chen L Y, Ding J L, Song D H, et al. Exploring scientific contributions through citation context and division of labor[J]. Scientometrics, 2025, 130(5): 2901-2921.
36 Júnior E A C, Silva F N, Costa L D F, et al. Patterns of authors contribution in scientific manuscripts[J]. Journal of Informetrics, 2017, 11(2): 498-510.
37 Bozeman B, Gaughan M. How do men and women differ in research collaborations? An analysis of the collaborative motives and strategies of academic researchers[J]. Research Policy, 2011, 40(10): 1393-1402.
38 Shibayama S, Baba Y, Walsh J P. Organizational design of University laboratories: task allocation and lab performance in Japanese bioscience laboratories[J]. Research Policy, 2015, 44(3): 610-622.
39 Haeussler C, Sauermann H. Division of labor in collaborative knowledge production: the role of team size and interdisciplinarity[J]. Research Policy, 2020, 49(6): 103987.
40 Baerlocher M O, Newton M, Gautam T, et al. The meaning of author order in medical research[J]. Journal of Investigative Medicine, 2007, 55(4): 174-180.
41 Yang S L, Wolfram D, Wang F F. The relationship between the author byline and contribution lists: a comparison of three general medical journals[J]. Scientometrics, 2017, 110(3): 1273-1296.
42 Davenport E, Cronin B. Who dunnit? Metatags and hyperauthorship[J]. Journal of the American Society for Information Science and Technology, 2001, 52(9): 770-773.
43 Smith E. “Technical” contributors and authorship distribution in health science[J]. Science and Engineering Ethics, 2023, 29(4): Article No.22.
44 Liu X, Zhang C J, Li J. Conceptual and technical work: Who will disrupt science?[J]. Journal of Informetrics, 2023, 17(3): 101432.
45 Adams J D, Black G C, Clemmons J R, et al. Scientific teams and institutional collaborations: evidence from U.S. universities, 1981-1999[J]. Research Policy, 2005, 34(3): 259-285.
46 Lee Y N, Walsh J P, Wang J. Creativity in scientific teams: unpacking novelty and impact[J]. Research Policy, 2015, 44(3): 684-697.
47 Becker G S, Murphy K M. The division of labor, coordination costs, and knowledge[J]. The Quarterly Journal of Economics, 1992, 107(4): 1137-1160.
48 丁敬达, 王新明. 基于作者贡献声明的合著者贡献率测度方法[J]. 图书情报工作, 2019, 63(16): 95-102.
49 Rao R I K, Sahoo B B. Distributions of multiple authors: a case study of two journals (JASIST and Scientometrics)[J]. COLLNET Journal of Scientometrics and Information Management, 2008, 2(1): 27-36.
50 Lindsey D. Production and citation measures in the sociology of science: the problem of multiple authorship[J]. Social Studies of Science, 1980, 10(2): 145-162.
51 Hagen N T. Harmonic publication and citation counting: sharing authorship credit equitably-not equally, geometrically or arithmetically[J]. Scientometrics, 2010, 84(3): 785-793.
52 Egghe L, Rousseau R, Van Hooydonk G. Methods for accrediting publications to authors or countries: consequences for evaluation studies[J]. Journal of the American Society for Information Science, 2000, 51(2): 145-157.
53 Abbas A M. Weighted indices for evaluating the quality of research with multiple authorship[J]. Scientometrics, 2011, 88(1): 107-131.
54 张丽华, 曾文. 无实质贡献署名作者的识别方法研究进展[J]. 图书情报导刊, 2023, 8(6): 60-69.
55 林鉴非. 农业科技期刊标注“作者贡献声明”的必要性探讨[J]. 农业图书情报学刊, 2017, 29(12): 168-170.
56 王岩哲, 刘凤红. 作者贡献声明理论研究及其实践应用进展综述[J]. 中国科技期刊研究, 2022, 33(9): 1186-1195.
57 徐晨. 多场景下单个科研成果合著贡献度研究[J]. 图书情报工作, 2015, 59(19): 93-99.
58 张梦莹, 章成志, 王杰. 不同学科的作者贡献分布差异研究——以图情和医学领域的四种期刊为例[J]. 图书馆论坛, 2018, 38(12): 112-119.
59 刘宇, 伍丹炜, 魏瑞斌. 知识贡献、身份地位与荣誉归属: 中文情境下的合作署名规律研究[J]. 图书馆论坛, 2024, 44(3): 96-105.
60 Wang H C, Fu T F, Du Y Q, et al. Scientific discovery in the age of artificial intelligence[J]. Nature, 2023, 620(7972): 47-60.
61 Kenekayoro P. Identifying named entities in academic biographies with supervised learning[J]. Scientometrics, 2018, 116(2): 751-765.
62 Chen H H, Wu L, Chen J P, et al. A comparative study of automated legal text classification using random forests and deep learning[J]. Information Processing & Management, 2022, 59(2): 102798.
63 Simionescu M. Econometrics of sentiments-sentometrics and machine learning: the improvement of inflation predictions in Romania using sentiment analysis[J]. Technological Forecasting and Social Change, 2022, 182: 121867.
64 Senior A W, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning[J]. Nature, 2020, 577(7792): 706-710.
65 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873): 583-589.
66 Baek M, DiMaio F, Anishchenko I, et al. Accurate prediction of protein structures and interactions using a three-track neural network[J]. Science, 2021, 373(6557): 871-876.
67 Lehr S A, Caliskan A, Liyanage S, et al. ChatGPT as research scientist: probing GPT’s capabilities as a research librarian, research ethicist, data generator, and data predictor[J]. Proceedings of the National Academy of Sciences of the United States of America, 2024, 121(35): e2404328121.
68 李忆, 喻靓茹, 邱东. 人与人工智能协作模式综述[J]. 情报杂志, 2020, 39(10): 137-143.
69 李慧, 李贵卿. 人工智能时代人机协作工作模型构建研究[J]. 现代管理, 2020, 10(3): 360-367.
70 Dergaa I, Chamari K, Zmijewski P, et al. From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing[J]. Biology of Sport, 2023, 40(2): 615-622.
71 Alshater M M. Exploring the role of artificial intelligence in enhancing academic performance: a case study of ChatGPT[J]. Social Science Research Network, 2022. DOI: 10.2139/ssrn.4312358.
72 Semrl N, Feigl S, Taumberger N, et al. AI language models in human reproduction research: exploring ChatGPT’s potential to assist academic writing[J]. Human Reproduction, 2023, 38(12): 2281-2288.
73 Khlaif Z N, Mousa A, Hattab M K, et al. The potential and concerns of using AI in scientific research: ChatGPT performance evaluation[J]. JMIR Medical Education, 2023, 9: e47049.
74 ?vab I, Klemenc-Keti? Z, Zupani? S. New challenges in scientific publications: referencing, artificial intelligence and ChatGPT[J]. Zdravstveno Varstvo, 2023, 62(3): 109-112.
75 孙蒙鸽, 韩涛, 王燕鹏, 等. GPT技术变革对基础科学研究的影响分析[J]. 中国科学院院刊, 2023, 38(8): 1212-1224.
76 Fügener A, Grahl J, Gupta A, et al. Collaboration and delegation between humans and AI: an experimental investigation of the future of work[J]. Social Science Research Network, 2019. DOI: 10.2139/ssrn.3368813.
77 Vaccaro M, Almaatouq A, Malone T. When combinations of humans and AI are useful: a systematic review and meta-analysis[J]. Nature Human Behaviour, 2024, 8(12): 2293-2303. |
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