Difference between revisions of "Scientific and technical human capital"
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== Empirical Application == | == Empirical Application == | ||
| − | Following from the work of Stephan and Levin (1997)<ref name="stephan_levin_1997 | + | Following from the work of Stephan and Levin (1997)<ref name="stephan_levin_1997>Stephan, P. E. & Levin, S.G. (1997). The Critical Importance of Careers in Collaborative Scientific Research. ''Revue d’économie industrielle 79(1)'', 45–61. Retrieved from http://www.persee.fr/web/revues/home/prescript/article/rei_0154-3229_1997_num_79_1_1652</ref> to some extent, empirical studies utilizing the S&T human capital approach have made a considerable contribution to understanding the role of research collaboration in research careers. For example, collaboration with industry has been shown to have beneficial effects on scientific productivity (Lee & Bozeman, 2005; Lin & Bozeman, 2006). Different collaboration strategies among researchers have been linked to different sets of motivational factors (Bozeman & Corley, 2004; Bozeman & Gaughan, 2011). While earlier studies (Bozeman & Corley, 2004; Lee & Bozeman, 2005) suggested men have greater numbers of collaborators than women, a more recent study found women and men to have similar levels of research collaboration – prompting suggestions that policies promoting gender equity in U.S. university careers may be paying dividends (Bozeman & Gaughan, 2011, p. 1399). |
Dietz and Bozeman (2005) look at the impact of time spent/jobs (n) in the private sector on scientific productivity measured exclusively as publications and patents. An index of career ‘homogeny’ is constructed to measure the extent to which an individual career conforms to a very standard or normal vision of an academic career (PhD, post doc, assistant Prof, tenured Prof). They found that among research centre based academics there is significantly more time being spent in the private sector (often through dual appointments) than life course productivity literature had shown in the past. This may be because the centres in the study were mission centres funded by government and hence, perhaps more likely to be strongly engaged with industry issues and problems. Career diversity was found to create some productivity boosts around both sides of a ‘job transition’ – so-called because of dual and hybrid appointments (industry PhDs). The overall change in universities toward a more ‘business’ model was also seen as a potential explanator for some of the emergence of these different career structures (see Lam, 2005). This problematic makes it seemingly important to better distinguish between career steps or job transformations (such as taking on a dual industry appointment or chairing a spin-off) and changes (moving from one University research centre to another). | Dietz and Bozeman (2005) look at the impact of time spent/jobs (n) in the private sector on scientific productivity measured exclusively as publications and patents. An index of career ‘homogeny’ is constructed to measure the extent to which an individual career conforms to a very standard or normal vision of an academic career (PhD, post doc, assistant Prof, tenured Prof). They found that among research centre based academics there is significantly more time being spent in the private sector (often through dual appointments) than life course productivity literature had shown in the past. This may be because the centres in the study were mission centres funded by government and hence, perhaps more likely to be strongly engaged with industry issues and problems. Career diversity was found to create some productivity boosts around both sides of a ‘job transition’ – so-called because of dual and hybrid appointments (industry PhDs). The overall change in universities toward a more ‘business’ model was also seen as a potential explanator for some of the emergence of these different career structures (see Lam, 2005). This problematic makes it seemingly important to better distinguish between career steps or job transformations (such as taking on a dual industry appointment or chairing a spin-off) and changes (moving from one University research centre to another). | ||
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== Sources == | == Sources == | ||
<references/> | <references/> | ||
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| + | [[Category:Research Career Theories]] | ||
Revision as of 15:52, 22 February 2018
Contents
Main
Bozeman and colleagues (2001, p. 718)[1] more abstract formulation of ‘scientific and technical human capital’ pairs an ‘expanded notion of human capital’ with a ‘productive social capital network´. Or alternatively, ‘the sum of researchers’ professional network ties and their technical skills and resources’ (Bozeman & Corley 2004, p. 599[2]). They argue that educational qualifications should not be understood as either an indicator of homogeneous human capital or as an end point in human capital acquisition. Rather both training and experience are heterogeneous, and individual scientific careers are somewhat unique trajectories of ongoing human capital accumulation.
Scientists’ technical human capital is defined by three dimensions:
- Cognitive skills – those cognitive abilities (maths reasoning, memory, ability to synthesize) that are largely independent of context or more likely interact but are not determined by context. Not only ‘scientific’ abilities (2001, p. 726[1])
- Substantive scientific and technical knowledge – formation and education, understanding or experimental and research findings (2001, p. 727[1])
- Context skills – knowledge accumulated by doing and creating and including tacit knowledge, craft skills, and knowledge specific to the design and implementation of specific research or experimentation plans (2001, p. 727[1]) not directly applicable but provide heuristics and analogies for other contexts.
The extent to which an individual scientist has particular ‘loadings’ of these factors will shape their career path. Evolution in capacities of these dimensions over time also shapes the possibilities in terms of career trajectories. These dimensions overlap and are in part co-constitutive of each other, but relative weightings determine to some degree the kinds of work roles within teams or other collectives that a scientist is most suited for (cf. habitus, Bourdieu, 1987[3]).
However, human capital represents only half the resources available to scientists, the remainder being available through a researchers’ accumulated social capital. Social capital is embodied in the sum of professional and personal interactions and relationships in which an individual is embedded and which increase the resources available to them.
Social capital is defined along two dimensions:
- The institutional setting of the network partner (firm, NGO, Govt institute, etc.)
- Role of the partner (entrepreneur, colleague, funding agency, etc.)
These dimensions combine into the social capital network. In terms of the formal analysis of a social capital network, insights from Social Network Analysis (SNA; Burt, 1992[4]; Granovetter, 1973[5]) also show that the configuration of extended network roles in terms of centrality, density and brokerage can also affect the resources that are available to an individual. Positioning within networks thus has career implications as well.
The most important parts of the social capital network are the ‘knowledge value collectives’ in which a researcher is involved. Knowledge value collectives (KVCs) are a ‘set of individuals connected by their uses of a body of scientific and technical knowledge’ and are smaller and less durable than scientific disciplines (Bozeman & Rogers, 2002, p. 777[6]). The basis for choices of research questions or collaborators in the S&T human capital model may thus be somewhat different to that in a model of disciplinary peer communities.
Empirical Application
Following from the work of Stephan and Levin (1997)[7] to some extent, empirical studies utilizing the S&T human capital approach have made a considerable contribution to understanding the role of research collaboration in research careers. For example, collaboration with industry has been shown to have beneficial effects on scientific productivity (Lee & Bozeman, 2005; Lin & Bozeman, 2006). Different collaboration strategies among researchers have been linked to different sets of motivational factors (Bozeman & Corley, 2004; Bozeman & Gaughan, 2011). While earlier studies (Bozeman & Corley, 2004; Lee & Bozeman, 2005) suggested men have greater numbers of collaborators than women, a more recent study found women and men to have similar levels of research collaboration – prompting suggestions that policies promoting gender equity in U.S. university careers may be paying dividends (Bozeman & Gaughan, 2011, p. 1399). Dietz and Bozeman (2005) look at the impact of time spent/jobs (n) in the private sector on scientific productivity measured exclusively as publications and patents. An index of career ‘homogeny’ is constructed to measure the extent to which an individual career conforms to a very standard or normal vision of an academic career (PhD, post doc, assistant Prof, tenured Prof). They found that among research centre based academics there is significantly more time being spent in the private sector (often through dual appointments) than life course productivity literature had shown in the past. This may be because the centres in the study were mission centres funded by government and hence, perhaps more likely to be strongly engaged with industry issues and problems. Career diversity was found to create some productivity boosts around both sides of a ‘job transition’ – so-called because of dual and hybrid appointments (industry PhDs). The overall change in universities toward a more ‘business’ model was also seen as a potential explanator for some of the emergence of these different career structures (see Lam, 2005). This problematic makes it seemingly important to better distinguish between career steps or job transformations (such as taking on a dual industry appointment or chairing a spin-off) and changes (moving from one University research centre to another).
Contributions to measurement concepts
Research collaboration and networking
This concerns social capital & networks.
Inter-sectoral mobility
This is related to the work-experience of individuals.
Sources
- ↑ 1.0 1.1 1.2 1.3 Bozeman, B., Dietz, J. S. & Gaughan, M. (2001). Scientific and Technical Human Capital: An Alternative Model for Research Evaluation. International Journal of Technology Management 22(7/8), 716. https://doi.org/10.1504/IJTM.2001.002988
- ↑ Bozeman, B. & Corley, E. (2004). Scientists’ Collaboration Strategies: Implications for Scientific and Technical Human Capital. Research Policy 33(4), 599–616. https://doi.org/10.1016/j.respol.2004.01.008
- ↑ Bourdieu, P. (1987). Die feinen Unterschiede. Kritik der gesellschaftlichen Urteilskraft. Frankfurt/Main: Suhrkamp
- ↑ Burt, R.S. (1992). Structural holes: the social structure of competition. Cambridge, MA: Harvard University Press.
- ↑ Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology 78, 1360–1380.
- ↑ Bozeman, B. & Rogers, J. D. (2002). A Churn Model of Scientific Knowledge Value: Internet Researchers as a Knowledge Value Collective. Research Policy 31(5), 769–94. https://doi.org/10.1016/S0048-7333(01)00146-9
- ↑ Stephan, P. E. & Levin, S.G. (1997). The Critical Importance of Careers in Collaborative Scientific Research. Revue d’économie industrielle 79(1), 45–61. Retrieved from http://www.persee.fr/web/revues/home/prescript/article/rei_0154-3229_1997_num_79_1_1652
