A framework towards bias-free contextual productivity assessment

Productivity assessment of various actors is one of the major concerns of Scientometrics and is vital for many applications that include policymaking. Popular productivity indices are not suitable for the determination of productivity of actors within a research context. A framework for the generation of metrics for contextual productivity assessment based on network approach has been recently proposed. However, that framework used full counting or full credit allocation, which incurs inflationary and equalizing bias. Schemes such as fractional and harmonic counting could reduce inflationary bias and harmonic counting has a repute of minimizing equalizing bias. As the existing framework for contextual productivity assessment is prone to inflationary and equalizing bias, empowering it with the provision to determine the right credit allocation scheme might take us closer to the achievement of a bias-free framework. In this work, a method to quantify the biases and to decide the right credit allocation scheme is introduced and using this we revamp the existing framework. As a case study, the productivity of inventors in the field ‘Wireless Power Transmission’ is determined. Implications from the real-world case study signify the effectiveness of the framework.