carlodebernardi

carlodebernardi.github.io

My personal webpage

SciSci-textual-methods

Repo for a class on quantitative textual analysis of science.

Simulating-epistemic-bias-in-italian-Academic-recruiting

According to some authors (e.g. Gillies 2014, Viola 2017), when researchers are called to express a judgment over their peers, they might exhibit an epistemic bias that make them favouring those who belong to their School of Thought (SoT). A dominant SoT is also most likely to provide some advantage to its members' bibliometric indexes, because more people potentially means more citations. In the long run, even the slight preference for one SoT over the others might lead to a monopoly, hampering the oft-invoked pluralism of research. In academic recruitment, given that those who recruited to permanent position will often become the recruiter of tomorrow, such biases might give rise to a self-reinforcing loop. However, the way in which this dynamics unfolds is affected by the institutional infrastructure that regulates academic recruitment. In order to reason on how the import of epistemic bias changes across various infrastructures, we built a simple Agent-Based Model using NetLogo 6.0.4., in which researchers belonging to rival SoTs compete to get promoted to professors. The model allows to represent the effect of epistemic and bibliometric biases, as well as to figure out how they get affected by the modification of several parameters.

lewebslinger.github.io

adso

Adso is a Topic Modelling library

carlodebernardi/carlodebernardi.github.io

My personal webpage

carlodebernardi/SciSci-textual-methods

Repo for a class on quantitative textual analysis of science.

carlodebernardi/Simulating-epistemic-bias-in-italian-Academic-recruiting

According to some authors (e.g. Gillies 2014, Viola 2017), when researchers are called to express a judgment over their peers, they might exhibit an epistemic bias that make them favouring those who belong to their School of Thought (SoT). A dominant SoT is also most likely to provide some advantage to its members' bibliometric indexes, because more people potentially means more citations. In the long run, even the slight preference for one SoT over the others might lead to a monopoly, hampering the oft-invoked pluralism of research. In academic recruitment, given that those who recruited to permanent position will often become the recruiter of tomorrow, such biases might give rise to a self-reinforcing loop. However, the way in which this dynamics unfolds is affected by the institutional infrastructure that regulates academic recruitment. In order to reason on how the import of epistemic bias changes across various infrastructures, we built a simple Agent-Based Model using NetLogo 6.0.4., in which researchers belonging to rival SoTs compete to get promoted to professors. The model allows to represent the effect of epistemic and bibliometric biases, as well as to figure out how they get affected by the modification of several parameters.