“Blue” social capital and work performance: anthropological fieldwork among crew members at four Danish international ships

One outcome of globalization is an increasing number of seafarers, who seldom are members of the same crew two voyages in a row and must cooperate with seafarers from many countries (Progoulaki and Roe WMU J Marit Affairs 10:7–23, 2011). This situation challenges the level of social capital on board, i.e., the resources inherent in network cooperation associated with norms of reciprocity and trust (Putnam 2000: 19). Fragmentized “blue” social capital should, however, be restored, as work performance depends on the quality of cooperation among crew members horizontally, as well as between crew members and the shipping company vertically. Drawing on anthropological fieldwork data from four Danish international ships, the purpose of this paper is to offer a new theoretical framework for analyzing cooperation among crews. Our inductive empirical findings suggest that a balance between three types of social capital—bonding, bridging and linking—is needed to achieve a high-performing work system (Gittell et al. Organ Sci 21(2):490–506, 2010). Hence, the main actors within the shipping sector should take “blue” social capital into account to increase work efficiency and economic performance.     

Using the SKAGEX dataset for evaluation of ocean model skills

Numerical ocean models are now being applied in numerous oceanographic studies. However, the qualities of the model results are often uncertain and there is a great need for standards and procedures for evaluation of the skills of numerical general circulation models. In this paper measurements from repeated hydrographical sections across Skagerrak taken in 1990, the SKAGEX dataset, are used to evaluate the skills of two σ-coordinate ocean models and to study the sensitivity of these models to model parameters. A methodology for quantification of model skills based on observations from repeated hydrographical sections in general is suggested. Area averages of absolute differences are for Skagerrak completely dominated by the discrepancies in the upper few meters of the ocean and may not be used to assess models' abilities to reproduce the fields in the larger and deeper part of the ocean. Therefore, discrepancies between average values in time from the observed fields and time averaged values from model outputs are related to the natural variability of the fields. The numbers produced with the suggested measure are relative numbers that will be specific for each section and for each series of observation. Ideally we would therefore like to see the measures computed for a number of sections for various models and choices of model parameters in order to assess model skills. The value of the SKAGEX dataset as a tool for model improvements is demonstrated. Evidence to support the importance of applying non-oscillatory, gradient preserving advection schemes in areas with sharp density fronts is given. The method is used to identify that the forcing/initial values/boundary values for the temperature field are inferior to the corresponding values for the salinity field. With the present coarse resolution, 11 layers in the vertical, it is shown that it is far from obvious that the quality of the model results improve when replacing simple Richardson number formulations for vertical mixing processes with higher order turbulence closure in the Skagerrak area.