Evaluating research and researchers by the journal impact factor: is it better than coin flipping?

The journal impact factor (JIF) is the average of the number of citations of the papers published in a journal, calculated according to a specific formula; it is extensively used for the evaluation of research and researchers. The method assumes that all papers in a journal have the same scientific merit, which is measured by the JIF of the publishing journal. This implies that the number of citations measures scientific merits but the JIF does not evaluate each individual paper by its own number of citations. Therefore, in the comparative evaluation of two papers, the use of the JIF implies a risk of failure, which occurs when a paper in the journal with the lower JIF is compared to another with fewer citations in the journal with the higher JIF. To quantify this risk of failure, this study calculates the failure probabilities, taking advantage of the lognormal distribution of citations. In two journals whose JIFs are ten-fold different, the failure probability is low. However, in most cases when two papers are compared, the JIFs of the journals are not so different. Then, the failure probability can be close to 0.5, which is equivalent to evaluating by coin flipping.Comment: 24 pages, 1 figure and 4 tables in the main text, plus 1 table as supplementary material. One single pdf fil

Analysis of a chemotaxis system modeling ant foraging

In this paper we analyze a system of PDEs recently introduced in [P. Amorim, {\it Modeling ant foraging: a {chemotaxis} approach with pheromones and trail formation}], in order to describe the dynamics of ant foraging. The system is made of convection-diffusion-reaction equations, and the coupling is driven by chemotaxis mechanisms. We establish the well-posedness for the model, and investigate the regularity issue for a large class of integrable data. Our main focus is on the (physically relevant) two-dimensional case with boundary conditions, where we prove that the solutions remain bounded for all times. The proof involves a series of fine \emph{a priori} estimates in Lebesgue spaces.Comment: 39 page

Recruitment and selection in organizations

This paper studies employer recruitment and selection of job applicants when productivity is match-specific. Job-seekers have private, noisy assessments of their match value and the firm performs noisy interviews. Job-seekers' willingness to undergo a costly hiring process will depend both on the wage paid and on the perceived likelihood of being hired, while a noisy interview leads the firm to consider the quality of the applicant pool when setting hiring standards. I characterize job-seekers' equilibrium application decision as well as the firm's equilibrium wage and hiring rule. I show that changes in the informativeness of job-seekers assessments, or changes in the informativeness of the firm's interview, affect the size and composition of the applicant pool, and can raise hiring costs when it dissuades applications. As a result, the firm may actually favor noisier interviews, or prefer to face applicants that are less certain of their person-job/organization fit