In most fields of science, medicine, and technology research, men comprise more than half of the workforce, particularly at senior levels. Most previous work has concluded that the gender gap is smaller today than it was in the past, giving the impression that there will soon be equal numbers of men and women researchers and that current initiatives to recruit and retain more women are working adequately. Here, we used computational methods to determine the numbers of men and women authors listed on >10 million academic papers published since 2002, allowing us to precisely estimate the gender gap among researchers, as well as its rate of change, for most disciplines of science and medicine. We conclude that many research specialties (e.g., surgery, computer science, physics, and maths) will not reach gender parity this century, given present-day rates of increase in the number of women authors. Additionally, the gender gap varies greatly across countries, with Japan, Germany, and Switzerland having strikingly few women authors. Women were less often commissioned to write ‘invited’ papers, consistent with gender bias by journal editors, and were less often found in authorship positions usually associated with seniority (i.e., the last-listed or sole author). Our results support a need for further reforms to close the gender gap.

Holman L, Stuart-Fox D, Hauser CE (2018) The gender gap in science: How long until women are equally represented? PLoS Biol 16(4): e2004956. https://doi.org/10.1371/journal.pbio.2004956

Source: journals.plos.org

Many have heard of Alan Turing, the mathematician and logician who invented modern computing in 1935. They know Turing, the cryptologist who cracked the Nazi Enigma code, helped win World War II. And they remember Turing as a martyr for gay rights who, after being prosecuted and sentenced to chemical castration, committed suicide by eating an apple laced with cyanide in 1954.

But few have heard of Turing, the naturalist who explained patterns in nature with math. Nearly half a century after publishing his final paper in 1952, chemists and biological mathematicians came to appreciate the power of his late work to explain problems they were solving, like how zebrafish get their stripes or cheetahs get spots. And even now, scientists are finding new insights from Turing’s legacy.

Source: www.nytimes.com

Perceptual generalization and discrimination are fundamental cognitive abilities. For example, if a bird eats a poisonous butterfly, it will learn to avoid preying on that species again by generalizing its past experience to new perceptual stimuli. In cognitive science, the “universal law of generalization” seeks to explain this ability and states that generalization between stimuli will follow an exponential function of their distance in “psychological space.” Here, I challenge existing theoretical explanations for the universal law and offer an alternative account based on the principle of efficient coding. I show that the universal law emerges inevitably from any information processing system (whether biological or artificial) that minimizes the cost of perceptual error subject to constraints on the ability to process or transmit information.

Efficient coding explains the universal law of generalization in human perception
Chris R. Sims

Science 11 May 2018:
Vol. 360, Issue 6389, pp. 652-656
DOI: 10.1126/science.aaq1118

Source: science.sciencemag.org

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

Academic performance and behavioral patterns

Valentin Kassarnig, Enys Mones, Andreas Bjerre-Nielsen, Piotr Sapiezynski, David Dreyer Lassen and Sune Lehmann
EPJ Data Science 2018 7:10
https://doi.org/10.1140/epjds/s13688-018-0138-8

Source: epjdatascience.springeropen.com