What are the chances that professors who have ascended to the top of the academic ladder through corrupt practices will uphold high standards of ethics and integrity?

In a recent editorial in the scientific journal Genes, Genomes, Genetics, the editor-in-chief, a professor at the University of Florida with a Scopus h-index of 45, highlights a critical but often overlooked point: citations are the primary way we recognize members of the global scientific community. Consequently, academics with no citations—or only a shockingly low number, including some full professors—are effectively excluded from this community, exposed as impostors. https://academic.oup.com/g3journal/article/14/7/jkae102/7709035

This situation raises a crucial question: can we reasonably expect those who have reached the highest academic ranks through nepotism or corruption to uphold the principles of ethics, merit, and intellectual integrity? More pressing still: shouldn’t the scientific community assume responsibility for identifying and confronting such individuals—through measures such as professional boycotts or by excluding them from editorial boards, funding panels, and other positions of academic influence?

In my country, I've noticed that some academics achieve full professorships through nepotism or political connections, while top young scholars are forced to emigrate to other countries to secure such positions. This issue is not unique to my country; a few years ago, a study revealed rampant nepotism in Italian academia. These problems exist in many countries, not just in Southern Europe.

This prompted me to advocate, several years ago, that Portuguese professors without a minimum number of citations (a Scopus h-index lower than 10) should be dismissed for failing to produce relevant academic work. In 2021 and again in 2022, I compiled lists of full professors who had not produced a single paper with at least 150 citations on Scopus. Unsurprisingly, those named were not pleased.

PS - Not all citations hold the same value. Some citations are highly valuable, others have zero value (self-citations), and some (Google Scholar based) even have a negative impact. https://19-pacheco-torgal-19.blogspot.com/2024/05/paper-how-to-exploit-chatgpt-for-large.html

Catedrática afirma que há catedráticos que não fazem parte da comunidade científica

Num recente editorial da revista científica Genes, Genomes, Gentics, a editora-Chefe da mesma, catedrática da universidade da Florida (Scopus h-index=45) lembra aquilo que é, ou devia ser, mais ou menos óbvio, que as citações são a forma como reconhecemos aqueles que fazem parte da comunidade científica mundial. Resulta daqui que aqueles que não tem citações não fazem parte dessa comunidade, inclusive até mesmo catedráticos, sem citações.  https://academic.oup.com/g3journal/article/14/7/jkae102/7709035

PS - No contexto supra vale a pena recordar (ou revistar) o conteúdo do post do passado mês de Maio: "Nem todas as citações valem o mesmo: Há as que tem muito valor, as que tem valor nulo (auto-citações) e as de valor negativo" https://19-pacheco-torgal-19.blogspot.com/2024/05/nem-todas-as-citacoes-valem-o-mesmo-ha.html

The Economist - Predicting Environmental Collapses Before They Happen

Chinese researchers have demonstrated that artificial intelligence (AI) can accurately predict critical tipping points in complex systems, such as financial markets (e.g., the 2007-09 crisis), abrupt changes in ecosystems, floods, power outages, and many others. This breakthrough is detailed in a recent study published in Physical Review X and highlighted in the latest issue of The Economist.

In their study, the researchers utilized over two decades of satellite data on tree cover and precipitation in Central Africa to predict transitions from tropical forests to savannas. The AI algorithm identified that even a small decrease in annual precipitation could trigger a significant drop in tree cover, indicating a critical tipping point. This represents a significant advancement over previous studies, showcasing the potential of AI to predict critical events across various domains:

"The team then asked the algorithm to identify the conditions that drove the shift to savannah—or, in other words, to predict an oncoming phase transition. The answer was, as expected, down to annual rainfall. But the ai was able to go further. When annual rainfall dropped from 1,800mm to 1,630mm, the results showed that average tree cover dropped by only about 5%. But if the annual precipitation decreased from 1,630mm to about 1,620mm, the algorithm identified that average tree cover suddenly fell by more than 30% further. This would be a textbook critical transition. And by predicting it from the raw data, the researchers say they have broken new ground in this field. Previous work, whether with or without the assistance of ai, could not connect the dots so well. https://www.economist.com/science-and-technology/2024/07/17/ai-can-predict-tipping-points-before-they-happen

PS - In the same issue of The Economist, you'll find another compelling article titled "China as the West's Corporate R&D Lab: Can It Remain So?" To fully grasp the insights offered in this new piece, it's essential to consider the context provided by an earlier article from The Economist’s second week of June, which I previously analyzed in my post, "Tech Titans Clash: America vs. China in the Race for Innovation Supremacy" https://19-pacheco-torgal-19.blogspot.com/2024/06/tech-titans-clash-america-vs-china-in.html