Abstract
Science and technology policy academics and evaluators use co-authorship as a proxy for research collaboration despite knowing better. Anecdotally we understand that an individual might be listed as an author on a particular publication for numerous reasons other than research collaboration. Yet because of the accessibility and other advantages of bibliometric data, co-authorship is continuously used as a proxy for research collaboration. In this study, a national (US) sample of academic researchers was asked about their relationships with their closest research collaborators—some with whom respondents reported having co-authored and some with whom respondents reported not co-authoring. The results suggest there are numerous dimensions of co-authorship, the most influential of which is informal and relational and with little (directly) to do with intellectual and/or other resource contributions. Implications for theory and practice are discussed. Generally we advise academics and evaluators interested in tracking co-authorship as a proxy for collaboration to collect additional data beyond those available from popular bibliometric resources because such information means better-informed modeling and better-informed policy and management decision making.
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Notes
For the remainder of the article we use the term ascription. We avoid using the term attribution so as not to confuse our efforts here with the long- and well-established authorship attribution literature, which focuses on inferring the unobservable characteristics of an author from the content of the documents written by that author.
It would be worthwhile to consider a more in-depth investigation on the possible qualitative differences between single- and multi- authored papers. Anecdotally, it seems that highly influential, paradigm-shifting theoretical papers tend to be single-authored, while papers involving empirical, “normal science” work are more-likely, and better if, co-authored in comparison. In the qualitatively different case of individual theoretical breakthroughs, it appears to easier to justify to apply the authorship-as-cause view.
We don’t get more specific than this due to response bias and measurement reliability (Bozeman et al. 2012). First, collaborators can have very different views of their own and others’ contributions to the same project. Related, beyond formalized mentor–mentee relations (e.g., once mentees graduate and become “equal” to their advisers), it’s difficult to reliably track formalized resource-based relations amongst collaborators, for instance amongst co-PIs. This is one of the reasons the analysis below also includes separate equations by academic rank, to control for the changing context of individual careers and collaboration roles, especially clear with regards to relationship with mentors.
For example being a graduate student or adviser of a graduate student constitutes verifiable formalized human capital, social capital, and capital resource relations. Inter-organizational research partnerships are entered predominantly for resource-based reasons. And an individual’s view that a collaborator contributes intellectually as a guarantor is also valid. See “Formal resource-based relations” and “Informal resource-based relations” sections.
“Women in Science and Engineering: Network Access, Participation, and Career Outcomes,” a project funded by the National Science Foundation (Grant# REC-0529642).
Disciplines were selected based on the level of female representation in order to allow for comparison across male-dominated versus gender balanced fields of study. The fields were chemistry, biological sciences, earth and atmospheric sciences, electrical engineering, computer science, and physics.
Data were cleaned for incomplete responses. In the cleaning, non response due to bad addresses were also removed for the calculation of response rate. For example, 136 of the emails were “bounced back” due to a bad email address and 19 were “returned to sender” by the recipient universities email server. Follow-up calls were made but respondents could not be located in these cases.
The division based on tertile values is arbitrary and chosen to limit the number of separate equations run for the separate productivity groups. Also, since for theoretical purposes a general delineation of levels of productivity suffices, more granular division did not seem practical or necessary.
Conceptualizing the unit of analysis in this way combines the concept of a “collaboration” broadly defined with a more explicit look at the characteristics of the “relationship” that underpins this collaboration. In other words, such conceptualization allows a broader operationalization of the concept of scientists’ collaborations understood as a complex phenomenon anchored around a relation—i.e. an interaction between two agents that is based on specific mechanisms, and actions, and is also contingent on the properties of the agents forming the relationship. This allows simultaneously broadening the lenses through which to observe the relationships underlying collaborations (i.e. as encompassing more than co-authorships), yet keeping it focused enough so that the phenomenon of collaboration remains articulated and measurable through the incidence of specific relationship mechanisms and properties. Respondents were asked to nominate individuals whom they consider to be their “closest collaborators.” No operational definition of a “collaborator” was given to respondents, in order to elicit nominations based on respondent’s perceptions of what kinds if relationships constitute “close collaboration.” Once a respondent had identified a collaboration, it was possible to gather information about specific properties of such relationships, which in turn allows examining the extent to which the selected predictors (see “Hypotheses” section) co-vary with co-authorship. Similarly, given that the unit of analysis is each individual collaborative relationship (rather than the individual), the models below, as specified in “Hypotheses” section examine what properties of the relationship would affect the likelihood that it will yield a publication within the last 2 years. Thus the single dependent variable for this study and in the models below is a binary variable coded 1 if the collaboration has yielded a publication within the last 2 years, zero otherwise.
Per a (post-estimation) Wald test, the differences between the coefficients is statistically significant (P = 0.024).
13 % of all collaborations fall into this category, see Table 2
Wald test of equivalence of the coefficients on “3–6 years” and “>6 years” suggest that relationships longer than 6 years have larger positive effect on likelihood of co-authored publication than 3–6 year relationships (P = 0.04).
Though the approach suggests already that the scientists’ research capacity should incorporate social capital variables, it does not provide guidelines for operationalization. Further, the approach emphasizes social ties as capital or resources, not as relations per se. Accordingly, the approach is in our view inappropriately biased towards the accumulation of institutionally diverse connections, even though existing and homophilic connections are just as important component of co-authorship.
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Appendix: Excerpt from the survey questionnaire—Questions and definitions conditioning respondents’ answers to collaboration questions
Appendix: Excerpt from the survey questionnaire—Questions and definitions conditioning respondents’ answers to collaboration questions
III Your Network
An important focus of our study is on the work relationships that evolve in the science and engineering communities. The following sets of questions ask you about specific types of interactions with people you know. The data collected in these questions is critical for understanding research and other support relationships in the academic environment. Your completion of this survey is completely confidential and the people you identify will not know that you have named them in a survey.
The following questions ask you about individuals you have worked and collaborated with at your institution, as well as in other organizations (Collaboration includes proposal generation, working on a research project, writing/presenting an academic paper/book or book chapter, or developing industrial products or patents). Later, you will be asked additional questions about these individuals.
50. Over the past two academic years , which individuals at your university have been your closest research collaborators? Example: Chris Smith
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51. Over the past two academic years , who have been your closest research collaborators outside of your institution (including other academic institutions, government or industry?) Example: Chris Smith
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Ponomariov, B., Boardman, C. What is co-authorship?. Scientometrics 109, 1939–1963 (2016). https://doi.org/10.1007/s11192-016-2127-7
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DOI: https://doi.org/10.1007/s11192-016-2127-7