Warnings of declining research productivity: does Italy buck the trend?

The paper takes a scientometric approach to measure the change in research productivity of Italian academics before the COVID-19 pandemic outbreak. We propose a composite output/input bibliometric indicator and apply it at the field level, conducting a longitudinal analysis. Although the number of academics in the national academic system has decreased, we register very strong growth in both the number of publications and their scholarly impact. The growth in productivity, with only rare exceptions, crosses almost all fields. However, in areas that are traditionally very internationalized (Biology, Physics, and Chemistry), growth is less sustained than overall average, and also the variability of productivity across fields seems reduced. The main reason for this detail would be the smaller margins for improvement in the fields that had already reached high international standing. What emerges from the analysis goes counter to some alarms of declining scientific productivity at the global level. The Italian case is partly explained by the historic adoption of policies aimed at strengthening competitive mechanisms, in particular through the introduction of systems of performance-based research funding, and bibliometric accreditation for professorship

Were the Italian policy reforms to contrast favoritism and foster effectiveness in faculty recruitment successful?

In this work, we assess whether and to what extent the latest Italian national policy initiatives intended, among others, to contrast favoritism and foster recruitment effectiveness have resulted in the desired effects. To answer the question, we propose two related analyses. One compares the research performance ratings of recruits in two subsequent five-year periods, before and after the introduction of the above policy measures. The second analysis compares the effectiveness of recruitment by all Italian universities, in the above two subsequent five-year periods. The results from the comparisons show a decline of both unproductive and high-performing recruits, and no overall improvement in the effectiveness of recruitment

Predicting the future success of scientific publications through social network and semantic analysis

Abstract Citations acknowledge the impact a scientific publication has on subsequent work. At the same time, deciding how and when to cite a paper, is also heavily influenced by social factors. In this work, we conduct an empirical analysis based on a dataset of 2010–2012 global publications in chemical engineering. We use social network analysis and text mining to measure publication attributes and understand which variables can better help predicting their future success. Controlling for intrinsic quality of a publication and for the number of authors in the byline, we are able to predict scholarly impact of a paper in terms of citations received 6 years after publication with almost 80% accuracy. Results suggest that, all other things being equal, it is better to co-publish with rotating co-authors and write the papers’ abstract using more positive words, and a more complex, thus more informative, language. Publications that result from the collaboration of different social groups also attract more citations