Version classiqueVersion mobile
OpenEdition Books

Connecting the Knowledge Commons — From Projects to Sustainable Infrastructure

 | 
Leslie Chan
, 
Pierre Mounier

Global Scholarly Collaboration

From Traditional Citation Practice to Direct Communication

Sergey Parinov et Victoria Antonova

Résumé

The development of recent research information systems allows a transformation of citations in the full text of research papers into interactive elements. Such interactivity in some cases works as an instrument of direct scholarly communications between citing and cited authors. We discuss this challenge for research e-infrastructure development including opportunities for improvements in research cooperation and in collaboration mechanisms for the global research community.

Texte intégral

Introduction

1Current achievements of scholarly digital publishing and citation content analysis create new ways for the better support of the Knowledge Commons. In this paper we discuss the integration of some tools from two ongoing projects. One offers a method of transforming citation data into “interactive elements”, another promises to transform the interactive elements into a communication instrument between citing and cited authors.

2Both projects are based on the principles of the Open Scholarly Infrastructure (Neylon et al.). This guarantees that even after completing the projects, their outputs will be available as a part of a sustainable infrastructure. It means that if there is a demand for the projects’ outputs, they can be freely re-used and developed by anyone.

3To discuss how current scholarly communication, based on publishing and citing papers, can be replaced by a collaboration between researchers based on direct communication, we should start with a clarification of the relationships between scholarly communication, cooperation, and collaboration.

4The main tool of scholarly communication for researchers with the research community is that of publishing their research. References in such publications serve as observable evidence of scholarly cooperation in the research community. Scholarly cooperation, as a kind of socio-economic cooperation (Smith et al.; Axelrod and Hamilton) means that some scholars use the outputs of other scholars and by this, they realize the collective development of scientific knowledge.

5Citation network analysis allows us to determine whose results the scholar used, i.e., with whom he had a cooperation and the position of the scholar in this network. For the research community as a whole, there is no other common method to indicate what and how outputs were used by particular scholars in their research.

6The forms of cooperation used to organize collective activity may vary depending on the number of participants in this activity and other conditions (Smith et al.). Processes of scholarly cooperation based on publishing and citing papers have the form of “horizontal cooperation” (Smith et al.) among the research community.

7Scholarly cooperation on a community-wide scale is observable and measurable because of:

  • current rules and regulations, which oblige scholars to specify their use of previously published research outputs in a prescribed manner;
  • tools and services for research papers preparation and publishing;
  • means for processing the full text of publications and visualizing citation data, relationships and statistics.

8As a result, the research community has technology to produce and share publications with citations as an instrument of cooperation. There are also formal/informal rules and social institutions that regulate the application of this cooperation instrument.

9Let us compare: scholarly cooperation based on the exchange of publications and citations with another well-known form of collective activity based on direct communication between members of a small group like a research laboratory or a project team.

10In the first case, the “cooperation is accomplished by the division of labor among participants as an activity where each person is responsible for solving a portion of the problem” (Power). If we look at how a small group works, as in the second case, we see a collaboration, which “is a coordinated, synchronous activity that is the result of a continued attempt to construct and maintain a shared conception of a problem” (Power).

11Such comparison shows that publications as a communication and cooperation tool offer participants significantly less effective cooperation. In particular, the authors of research publications do not know about all the facts when someone uses their published research. As a rule, they do not have accurate and complete information on who and when used (cited) their publications, what exactly was used (cited) and for what purposes. There are no common ways for authors to publicly respond to how other researchers used (cited) their publications. The research community does not have a complete picture of how the publication was used to create new scientific knowledge. Similarly for the authors: it is not known exactly which of their outputs were used, by whom and how exactly.

12If we consider citations as a reflection of scholarly cooperative links, then in real academic life the citations bear a lot of information, not all of the same weight. These aspects, including scholars’ lines of behavior associated with citations, and the actual content of citations are studied by the sociology of citations (Adler et al.). As the main type, the “grateful” citations (Adler et al.) are pointed out, considered as an acknowledgement of the intellectual debt in relation to the cited publication. Such citations directly indicate the scholarly cooperation between citing and cited authors.

13Some researchers, however, consider that for modern research publications the “rhetorical” citations (Adler et al.) are more common (Cozzens). They work as the means of conducting scientific discussion, serve as illustrations and also perform certain “ritual” functions, not directly related to the process of collective creation of scientific knowledge and scholarly cooperation.

14This situation is further aggravated by the use of citation indexes to assess research performance. Measuring the success of scholars by the number of citations their publications receive affects the essence of the citation process, because in many cases this indicator becomes the goal of scientific activity. Consequently, all the characteristics of the citation process cease to be trustworthy (Neylon).

15Despite this, the publication of research results and the practice of citation are mandatory in the research community. This allows us to suggest that the data on citation networks extracted from research publications carry information about a substantial part of the existing global network of scholarly cooperation. The processing of these data makes it possible to visualize this network and presumably identify potentially collaborative scholars.

16Identifying pairs of scholars, citing and cited, one can suppose that they are related by potential scholarly cooperation. If the contact information of both is available, it is possible to organize actual (direct) scholarly communication between them. When such communication becomes possible between all pairs of cooperative scholars, it promises them better research performance.

17The development of a technology for scholarly cooperation between citing and cited authors creates the conditions when researchers can identify the “suppliers” of the research results required by them, and the “consumers” of their own results. Hence, scholars are able to simultaneously coordinate their research in two directions with the “suppliers” of the research results required by them, and with the “consumers” of their own research.

18The next step is the creation of the conditions for the emergence of full-fledged collaboration between citing and cited scholars, which normally arises only in small groups.

19The second section of the paper considers some research information systems (RIS), the combination of which allows the citations found in research publications to be turned into interactive elements. Such innovation, in its turn, allows RIS to initiate direct communication between cited and citing authors.

20In the third section, we discuss how the small-group mechanism of collaboration works and what RIS functionality is required in order to create conditions for the operation of such mechanism on the scale of the entire research community.

21In conclusion, the possible consequences of cooperative mechanism implementation for the scientific community as a whole are briefly discussed.

From Citing of Papers to Direct Scholarly Communication

22There is a clear motivation to create a technology which allows direct scholarly communication between researchers who currently can only cite each other’s papers. As expected, such an opportunity for scholarly cooperation can greatly increase the research performance of these scholars.

23This technology is a combination of existing and newly created tools. Some new tools allow parsing of extended set of citation data from research papers, which also gives us the citation contents around the in-text citations. Within a RIS, the citation content can be used to initiate direct scholarly communication between citing and cited authors. A citation content analysis also helps to use such communications to develop research cooperation.

Parsing of Citation Data from Research Papers

24The project CyrCitEc1 is developing a tool for the parsing of citation data, including a context for in-text citation from full-text research papers in PDF (Parinov, “Semantic Attributes for Citation Relationships”). The project is funded by the Russian Presidential Academy of National Economy and Public Administration (RANEPA2). It takes input data from RIS RePEc3 and Socionet.4

25In late March 2018, CyrCitEc processed 203 collections of papers with 89,342 publications in total. The largest part of this set are 157 Russian academic journals covering different academic disciplines and provided by NEICON.5 There are also research paper series in Russian and English provided by Russian universities, including the Higher School of Economics6 and RANEPA.7

26The approach used by CyrCitEc for citation data parsing was presented in “Semantic Attributes for Citation Relationships: Creation and Visualization” (Parinov). All citation data extracted by the CyrCitEc project are publicly available at http://cirtec.ranepa.ru. Regularly updated CyrCitEc statistics about parsing results are available at http://cirtec.ranepa.ru/​stats.html.

27Only 69% of the papers have full text PDFs available for the citation data parsing and only 51% of the papers have a list of references in a more or less standard form.

28Based on the set of papers with references, we parsed in total 801,318 references, that is on average 18 references per paper. In this set, about 5% of references are duplicated, because different papers can cite the same publications and have the same references. Such low duplication rate is a consequence of a very multidisciplinary character of a processed collection of academic journals.

29For 26,467 of the parsed references, we were able to create citation relationships between citing and cited papers, since we found cited papers’ metadata within RePEc and Socionet information systems. It is only about 3% of total references because currently these research information systems don’t represent the majority of research papers.

30Using the same set of papers and approach, we parsed 750,607 in-text citations. They mention 1,072,175 parsed references. This is 270,857 references more than the total number of parsed references, since some references are mentioned more than once. On average, there are 1.3 mentions per reference.

31Non-mentioned references were also counted: 110,340 references (14%) have no in-text citations at all. About 37% of papers with references have at least one non-mentioned reference.

In-text Citations

32Among different types of citation data available in research papers, in-text citations may keep the data about the character of cooperation between citing and cited authors.

33The in-text citation data parsed by CytEcCyr include the following attributes (see also an example of data record below):

  • a text string of the style of in-text citation, e.g., a number or an author name in square or round brackets (the tag <Exact> in the example below);
  • a link to a reference, mentioned in this in-text citation (the tag <Reference> below);
  • text coordinates of the in-text citation, i.e., a serial number of the first and the last in-text citation symbols counting from the beginning of the paper’s content (tags <Start> and <End>);
  • citation contexts located on the left and on the right of the in-text citation; these include at least 200 symbols expanded for taking a whole sentence (tags <Prefix> and <Suffix>).

34An example of parsed data about one in-text citation

<intextref>
 <Prefix>… countries and Soviet republics</Prefix>
 <Suffix>; Gokhberg, Kuznetsova, 2011]. …</Suffix>
 <Start>8757</Start>
 <End>8781</End>
 <Exact>[Gokhberg et al., 2009</Exact>
 <Reference>20</Reference>
</intextref>

35The in-text citation from the example above has a link with a reference having the number 20 in a paper. CyrCitEc parsed for this reference the following data:

<reference num="20" start="54464" end="54654" 
 author="Gokhberg Kuznetsova Zaichenko" title="Towards …" 
 year="2009" 
 handle="repec:oup:scippl:v:36:y:2009:i:2:p:121-126">
 <from_pdf>Gokhberg L., Kuznetsova T., Zaichenko S. (2009) 
 Towards a New Role of Universities in Russia: Prospects and 
 Limitations. Science and Public Policy, vol. 36, no 2, pp. 121 – 126.</from_pdf>
</reference>

36All CyrCitEc data with in-text citations and related references are input for the next tool, which is a part of the Socionet RIS.

Socionet Tools

37Socionet services, as described in the article “Semantic Attributes for Citation Relationships: Creation and Visualization”, use in-text citations and references data to produce computer-generated annotations in the content of PDF papers (Parinov). Figure 1 shows what these annotations look like using in-text citation and reference data from the examples above.

Figure 1. An in-text citation as an interactive element

Figure 1. An in-text citation as an interactive element

Source: https://goo.gl/​bZJwzZ

38Readers of PDF papers see the in-text citations, if they exist, as annotated text. At figure 1 there are mentions of two references in brackets. These highlighted in-text citations work as interactive elements, since clicking on them opens an information box (below on figure 1) with additional data about the cited paper. The additional data can include details about the cited paper (citing statistics, title, authors, etc.) and links to additional tools.

39One of the Socionet features is the multiple semantic relationships between information entities (Parinov, “Towards a Semantic Segment of a Research e-Infrastructure”). It allows the linking of citation data with other types of information. A fragment of the semantic linkage network is presented at figure 2.

40Using these linkages, we can associate additional data with the citation data, e.g., the contact and affiliation data of the authors for the cited and citing papers, metadata of cited and citing papers, etc.

41As we expect, in 2019 CyrCitEc will process all available publications from RePEc and Socionet. As a result, the parsed citation data will be automatically linked with about 70,000 author profiles.8 These author profiles are linked with papers, and with about 15,000 profiles of organizations9 that also have links with other author profiles from their staff.

42The system, using the linkages, can also recognize types of relationships between signed-in users and information objects:

  • a user is the author of the browsed object (paper, citation, etc.);
  • a user is a co-author of the selected author;
  • a user is a citing or cited author;
  • a user is just a reader; they have no relation with the browsed object.

43This information allows the system to initiate different scenarios of direct communications (Kogalovsky and Parinov) between cited and citing authors.

Figure 2. Semantic linkage network, a fragment

Figure 2. Semantic linkage network, a fragment

44The system can notify an author about new citations of their papers, including their context. Well-known RIS ResearchGate, Academia.edu and others already do it. The system can also provide the author with links to PDFs, which cite their papers, where the new citations are highlighted/annotated and work as interactive elements or as a communication instrument.

45If the system identified a user as the author of the cited paper and this user clicks the in-text citation pointed to his cited paper, the system allows this user to express publicly or privately his reaction on how the citing author used/cited his research. According to the citation content, this reaction can be as simple as “agree/disagree” or can provide explanations by the cited author about what was wrong with using his outputs, or how it could be used properly, etc. If the cited paper has several co-authors the system allows them to express their “agree/disagree” with a reaction of one of them.

46The system itself also can initiate some direct communications between citing and cited authors using the sense of the citation content, such as citation polarity (author’s disposition toward a cited paper) or citation function (a purpose of citation) (Hernández-Alvarez and Gómez).

Citation Content Analysis

47Citation content analysis, for which CyrCitEc already provides about 750,000 records of the right and left contexts for each in-text citation, has a lot of attention from researchers. Waltman, in his review of the traditional citation impact indicators, proposed different ways to improve the indicators, including taking into account “the context in which a publication is referenced (i.e., the sentences in a citing publication around the reference to a cited publication)” (Waltman 43).

48In recent years, methods for analyzing the content of citations have been actively developed. Some studies (Zhang et al; Ding et al.) present concepts of content-based citation analysis (CCA), which addresses a citation’s value.

49Practical experiments with the analysis of in-text citations (they are also called as the in-text references) on various sets of full text papers are also known. One of them identified verbs in citation contexts (Bertin and Atanassova, “Factorial Correspondence Analysis”) and later they characterized the different sections of articles in terms of the verbs that appear in citation contexts (Bertin and Atanassova, “Factorial Correspondence Analysis”). Another aspect of CCA is how references are distributed along the structure of articles, and the age of these cited references (Bertin et al.). Some authors analyzed in-text citations as functions of time, textual progression, and scientific field. They built characteristics of in-text citations in over five million full text articles (Boyack et al.).

50Hernández-Alvarez and Gómez, in their survey of CCA, provided information about tasks, techniques, and resources, including such tasks as the citation polarity and function classifications.

51The analysis of citation polarity/function has the potential to draw conclusions about the motives of authors in citing papers. Such analysis can also produce suggestions: what exactly was used from the cited papers and why. In some cases, this information may be critically important to the authors of the cited papers and may help to initiate direct communication between them and the citing authors.

52If CCA recognizes criticism and the system notifies the author of the criticized paper, it gives them an opportunity to correct mistakes and further develop their research.

53If CCA informs the author about a positive impact of their paper, then the author can conclude how to develop their research to strengthen research results of other scholars who cited them.

54In both cases, cited authors will benefit if they inform citing authors about their progress with the cited research.

55As a result, citation networks will become true communication networks. When implemented, such a system should theoretically allow researchers to directly collaborate with each other without the mediation of the current publishing infrastructure.

56However, it wouldn’t be limited to pairs of communicating researchers. It is an act of, at least, direct triple communications since, if we observe a citing author, they can enter in direct communication, not only with authors they cited but also with authors who cited them.

57We should address the situation when researchers cooperate and coordinate their activity in a group that includes three parties:

  • themselves (“producers”);
  • authors whom they cite (“suppliers”);
  • authors who cite them (“consumers”).

58It means cooperation where “suppliers” and “consumers” also have direct communication and can directly affect each other’s’ activity. Since the group is “a collection of people committed to work jointly toward at least one group goal” (Randrup et al.), the goal of this group is obviously the creation of new scientific knowledge.

Towards a Global Scholarly Collaboration System

59By providing direct scholarly communications for the participants of scholarly virtual cooperation, who have traditionally collaborated via publication exchange and citation, it becomes theoretically possible to create more favorable conditions for their actual collaborative creativity and the development of new scientific knowledge.

60Let us consider a joint activity of scholars in small groups (i.e., laboratories, project teams, etc.), which are strongly based on direct communication between group members.

61A group, and specifically a small group, is “a distinguishable set of two or more people who interact, dynamically, interdependently, and adaptively toward a common and valued goal/objective/mission, who have each been assigned specific roles or functions to perform” (Mathieu et al. 274). A key feature of small groups is a harmonization of activities on the principle of “all with everyone”.

62Another important feature of small groups is the high variability of their environment and, as a consequence, the need for group members to quickly adapt to changing conditions. As Mathieu et al. underlined, “. . . in order to adapt effectively, team members must predict what their teammates are going to do and what they are going to need in order to do it” (Mathieu et al. 274).

63Cooperation is generally defined as a “joint effort toward a group goal” and as a “concerted collaboration”, which is a collaboration with “no identifiable individual deliverables; only group deliverables, toward which members must contribute simultaneous efforts” (Randrup et al.).

64Cooperation becomes “concerted collaboration”, if the participants can create a collective model of their activity and habitat. They need the collective model to “play” (simulate) and analyze the various possible options for cooperation. Such a collective model arises if the group members can share with the group their personal mental models and hence create a collective (team) mental model.

65The basic idea of mental models is that humans, by their mental reflection, construct internal working models of the world:

When interacting with the environment, with others, and with the artifacts of technology, people develop internal mental models of themselves and the things with which they are interacting. These models provide predictive and explanatory power for understanding these interactions. (Badke-Schaub et al. 7)

66The concept of a shared or collective mental model is defined as:

knowledge structures held by members of a team that enable them to form accurate explanations and expectations for the task, and, in turn, coordinate their actions and adapt their behavior to demands of the task and other team members. (Jonker et al.)

67The basis of the collaboration mechanism, which works for a small group, is that of shared mental models. A background in this area includes research on the development of public institutions (Denzau and North), increase in the effectiveness of joint activities of people in a group (Mathieu et al.), interaction of people with software agents (Fan and Yen), environmental protection (Jones et al.), political activities (Richards), etc.

68The sharing of group members’ mental models means that members inform each other about their intentions and possibilities regarding options of their joint activity. An aggregation of such information, received from all members, forms a choice area, which is available for analysis to each individual member.

Shared or team mental models are characterized as knowledge or belief structures that are shared by members of a team, which enable them to form accurate explanations and expectations about the task, and to coordinate their actions and adapt their behaviors to the demands of the task and other team members. (Badke-Schaub et al. 8)

69In “Toward a Theory and Agent-Based Model of the Networked Economy”, we proposed a conceptualization of how a collaboration mechanism based on shared mental models works (Parinov).

70An aggregation of members’ shared mental models creates a collective mental model of the team. Team members interact with the collective mental model by taking information from it, playing with it (sorting out different ways of their cooperation), and by changing it. They can change in the model their personal information image. They can also propose new configurations of the group’s cooperation.

71In figure 3 we illustrate these interactions by the example of forming a collective mental model (CMM) for a group of four members. Each member, by continuously exchanging information with others, forms and actualizes their own mental model of group cooperation and alienates it into collective model for decision making about the future group’s activity.

Figure 3. Collective mental model

Figure 3. Collective mental model

A source of this illustration, with more explanations and notations, was published in “Toward a Theory and Agent-Based Model of the Networked Economy” (Parinov).

72If group members have fixed in CMM a mutually acceptable configuration of their cooperation, then this configuration passes to the stage of practical implementation. The CMM, formed and set at this stage, is used by the members for the actual coordination of their practical activities.

73There is a lot of literature on collaboration research, such as the Six Patterns of Collaboration, which suggests a conceptualization of collaboration as the following processes: generate, reduce, clarify, organize, evaluate and build commitment (Briggs et al., A Six Layer Model of Collaboration). The same authors also provide the Six-Layer Model of Collaboration, which includes collaboration goals, group work products, group activities, group procedures, collaboration tools and collaborative behaviors (Briggs et al.). Such research claims an intellectual foundation (Randrup et al.) for discussing computer-supported collaboration, collaboration support systems (Briggs et al., “Facilitator-in-a-Box”), integrated collaboration environment (Vindasius) and many others.

74However, they do not address the basic collaboration instrument, that is, shared mental models. Without it, it is impossible to respond to the obvious research question: how can the mechanism of scholarly collaboration, which traditionally serves only members of small groups, work for the entire research community?

75In this paper, we do not claim to give an exhaustive explanation of the question posed. Below we discuss what main tasks should be solved to make CMM a part of the social and technical research infrastructure. One of possible options is to implement CMM in RIS, like RePEc or Socionet. Based on services of these RIS we can implement CMM to develop tools for direct communication between citing and cited authors. Since these RIS for economics have about 70,000 identified authors linked with their papers, the proposed mechanism of collaboration will serve a significant part of this research community.

76CMM within a RIS is already more than a “mental” model. In that case, it would be better to call it a collective information model (CIM).

77On the subject of having CIM as part of a social and technical research infrastructure, we suggest the following main tasks:

  • A synchronization of the individual mental models of the group's members with their information images in CIM.
    This function can be realized with the help of an information system that collects and accumulates various data about the activities, intentions and capabilities of cooperative researchers. Tools need to be developed so as to allow a person to share their mental model with the information system, and also to ensure its continuous realization.
    The parameters of the implementation of this function are: the number of group members reflected in the CIM; the accuracy and completeness of the representations of their behavior, intentions and possibilities; and the speed and accuracy of updating changes in these data.
  • Representation in CIM of the environment in which the group works, and a reflection of the changes taking place in this environment.
    An information system can also perform this function by collecting information about the environment where the group members cooperate. It is possible that in the near future this function will be implemented even more effectively in connection with the development of the “internet of things”.
    The parameters of the implementation of this function are: the size of the environment’s fragment reflected in the CIM; the accuracy and completeness of its representation; and the level of actualization of changes in the environment.
  • Playing (making simulations) in the CIM with possible variants of group members’ cooperation. Various computer simulation models can be useful to implement this function, allowing a computer analysis of the best scenarios.
  • Choosing the best variant for cooperation from many possible.
    With equal relations between group members (no subordination), the implementation of this function means that the members must negotiate. This requires a mechanism of collective decision-making.
  • Realization of the chosen variant of cooperation in practice, including management functions over the joint activities of the group members.
    This requires a mechanism for collective management of joint activities.

78Summing up the five points listed above, we can conclude that the implementation of the first three tasks are greatly influenced by a power of information technology, while the last two tasks also depend on social norms and rules, i.e., on institutions of cooperation.

Conclusion

79If the small-group mechanism of collaboration is used on a larger scale, a cooperation in the research community may work in a more effective mode. Cooperative scholars can coordinate their activities faster by direct communication and that will foster their research performance.

80Another consequence is that traditional publications and academic publishing infrastructure lose their monopoly as an instrument of global scholarly communication. This will create some challenges for sustainability of the global research community.

81Another serious challenge is the rapid increase of communication among cooperative researchers resulting in a danger of information overloading for them. As Randrup et al. wrote: “Core insight with a significant negative impact on the performance of collaboration is which have been unveiled by research is cognitive overload and inertia. Individuals have limited attention resources”. It is a threat, but current research and development in areas like software agents, computer bots and artificial intelligence help humans cope with an increasing intensity of information flows and give optimistic perspectives for surviving in the coming digital era.

Bibliographie

Adler, Robert, John Ewing and Peter Taylor. “Citation Statistics: A Report from the International Mathematical Union (IMU) in Cooperation with the International Council of Industrial and Applied Mathematics (ICIAM) and the Institute of Mathematical Statistics (IMS).” Statistical Science, vol. 24, no. 1, 2009, pp. 1-14.

Axelrod, Robert and William D. Hamilton. “The Evolution of Cooperation”. Science, vol. 211, no. 4489, 1981, pp. 1390-1396.

Badke-Schaub, Petra, Andre Neumann, Kristina Lauche and Susan Mohammed. “Mental Models in Design Teams: A Valid Approach to Performance in Design Collaboration?”. CoDesign, vol. 3, no. 1, 2007, pp. 5-20. doi:10.1080/15710880601170768

Bertin, Marc and Iana Atanassova. “A Study of Lexical Distribution in Citation Contexts through the IMRaD Standard.” BIR@ECIR, vol. 1143, 2014, pp. 5-12.

Bertin, Marc and Iana Atanassova. “Factorial Correspondence Analysis Applied to Citation Contexts.” BIR@ECIR, vol. 1344, 2015, pp. 22-29.

Bertin, Marc, Iana Atanassova, Yves Gingras and Vincent Larivière. “The Invariant Distribution of References in Scientific Articles.” Journal of the Association for Information Science and Technology, vol. 67, no. 1, 2016, pp. 164-177.

Boyack, Kevin. W., Nees Jan van Eck, Giovanni Colavizza and Ludo Waltman. “Characterizing in-text Citations in Scientific Articles: A Large-Scale Analysis.” Journal of Informetrics, vol. 12, no. 1, 2018, pp. 59-73.

Briggs, Robert Owen, Gwendolyn L. Kolfschoten, Gert-Jan de Vreede, Conan C. Albrecht, Stephan G. Lukosch and Douglas L. Dean. “A Six Layer Model of Collaboration.” A Six Layer Model of Collaboration, edited by M.E. Sharp, Armonk, 2014, pp. 211-228.

Briggs, Robert Owen, Gwendolyn L. Kolfschoten, Gert-Jan de Vreede, Conan C. Albrecht and Stephan G. Lukosch. “Facilitator-in-a-Box: Process Support Applications to Help Practitioners Realize the Potential of Collaboration Technology.” Journal of Management Information Systems, vol. 29, no. 4, 2013, pp. 159-194.

Cozzens, Susan E. “What Do Citations Count? The Rhetoric-First Model.” Scientometrics, vol. 15, no. 5–6, 1986, pp. 437-447. doi: 10.1007/BF02017064

Denzau, Arthur T. and Douglass C. North. “Shared Mental Models: Ideologies and Institutions.” Kyklos, vol. 47, no. 1, 1994, pp. 3-31.

Ding, Ying, Guo Zhang, Tamy Chambers, Min Song, Xialong Wang and Chengxiang Zhai. “Content-Based Citation Analysis: The Next Generation of Citation Analysis.” Journal of the Association for Information Science and Technology, vol. 65, no. 9, 2014, pp. 1820-1833.

Fan, Xiaoconq and John Yen. “Realistic Cognitive Load Modeling for Enhancing Shared Mental Models in Human-Agent Collaboration.” Proceedings of the 6th international joint conference on Autonomous agents and multi-agent systems, ACM, 2007, p. 60.

Hernández-Alvarez, Myriam and José M. Gómez. “Survey About Citation Context Analysis: Tasks, Techniques, and Resources.” Natural Language Engineering, vol. 22, no. 3, 2016, pp. 327-349.

Jones, Natalie A., Helen Ross, Timothy Lynam, Pascal Perez and Anne Leitch. “Mental Models: An Interdisciplinary Synthesis of Theory and Methods.” Ecology and Society, vol. 16, no. 1, 2011.

Jonker, Catholijn M., M. Birna van Riemsdijk and Bas Vermeulen. “Shared Mental Models: A Conceptual Analysis.” Coordination, Organizations, Institutions, and Norms in Agent Systems, vol. VI, 2011, pp. 132-151.

Kogalovsky, Mikhail and Sergey Parinov. “Scholarly Communication in a Semantically Enrichable Research Information System with Embedded Taxonomy of Scientific Relationships.” Knowledge Engineering and the Semantic Web, edited by Pavel Klinov and Dmitry Mouromtsev, Springer, 2015, pp. 87-101.

Mathieu, John E., Tonia S. Heffner, Gerald F. Goodwin, Eduardo Salas and Janis A. Cannon-Bowers. “The Influence of Shared Mental Models on Team Process and Performance.” Journal of Applied Psychology, vol. 85, no. 2, 2000, p. 273.

Neylon, Cameron. “Citation Metrics are Making Headlines, but What Does Citation Really Mean?” JISC blog, 8 December 2017, https://jisc.ac.uk/blog/citation-metrics-are-making-headlines-but-what-does-citation-really-mean-08-dec-2017.

Neylon, Cameron, Geoffrey Bilder and Jennifer Lin. “Principles for Open Scholarly Infrastructures.” Science in the Open, 2015. doi:10.6084/m9.figshare.1314859

Parinov, Sergey. “Semantic Attributes for Citation Relationships: Creation and Visualization”. Metadata and Semantics Research: 11th International Conference, MTSR 2017, Tallinn, Estonia, November 28 – December 1, 2017, Proceedings, edited by Emmanouel Garoufallou, Sirje Virkus, Rania Siatri and Damiana Koutsomiha, Springer, 2017, pp. 286-299.

Parinov, Sergey. “Towards a Semantic Segment of a Research e-Infrastructure: Necessary Information Objects, Tools and Services.” International Journal of Metadata, Semantics and Ontologies, vol. 8, no. 4, 2013, pp. 322-331. Preprint at Socionet: https://socionet.ru/publication.xml?h=repec:rus:mqijxk:32

Parinov, Sergey. “Toward a Theory and Agent-Based Model of the Networked Economy.” 1999. Preprint at Socionet: https://socionet.ru/publication.xml?h=repec:nos:econom:827080

Power, Lynn. “Collaboration vs. Cooperation. There Is a Difference.” Huffpost, 6 June 2016, updated 6 June 2017, https://www.huffingtonpost.com/lynn-power/collaboration-vs-cooperat_b_10324418.html

Randrup, Nils, Douglas A. Druckenmiller and Robert Owen Briggs. “Philosophy of Collaboration.” In 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE Computer Society Washington, 2016, pp. 898-907.

Richards, Diana. “Coordination and Shared Mental Models.” American Journal of Political Science, vol. 45, no. 2, 2001, pp. 259-276.

Smith, Ken G., Stephen J. Carroll and Susan J. Ashford. “Intra- and Interorganizational Cooperation: Toward a Research Agenda.” Academy of Management Journal, vol. 38, no. 1, 1995, pp. 7-23. doi:10.5465/256726

Vindasius, Julia. “The Integrated Collaboration Environment as a Platform for New Ways of Working: Lesson Learned from Recent Projects.” Society of Petroleum Engineers, 2008. doi:10.2118/112218-MS

Waltman, Ludo. “A Review of the Literature on Citation Impact Indicators.” Journal of Informetrics, vol. 10, no. 2, 2016, pp. 365-391.

Zhang, Guo, Ying Ding and Staša Milojević. “Citation Content Analysis (CCA): A Framework for Syntactic and Semantic Analysis of Citation Content.” Journal of the American Society for Information Science and Technology, vol. 64, no. 7, 2013, pp. 1490-1503.

Table des illustrations

Titre Figure 1. An in-text citation as an interactive element
Crédits Source: https://goo.gl/​bZJwzZ
URL http://books.openedition.org/oep/docannexe/image/9008/img-1.jpg
Fichier image/jpeg, 193k
Titre Figure 2. Semantic linkage network, a fragment
URL http://books.openedition.org/oep/docannexe/image/9008/img-2.jpg
Fichier image/jpeg, 192k
Titre Figure 3. Collective mental model
Légende A source of this illustration, with more explanations and notations, was published in “Toward a Theory and Agent-Based Model of the Networked Economy” (Parinov).
URL http://books.openedition.org/oep/docannexe/image/9008/img-3.jpg
Fichier image/jpeg, 323k

Auteurs

CEMI RAS, RANEPA
Higher School of Economics

Acheter

Volume papier

i6doc.com