What matters more to a scientist鈥檚 career success: where they currently work, or where they got their Ph.D.? It鈥檚 a question a team of researchers teases apart in a new paper published in PNAS. Their analysis calls into question a common assumption underlying academia: that a researcher鈥檚 productivity reflects their scientific skill, which is reflected in the prestige of their doctoral training.
It鈥檚 true that faculty at prestigious universities publish more scientific papers and receive more citations and awards than professors at lower-ranked institutions. It鈥檚 also true that prestigious schools tend to hire new faculty who hold Ph.D.s from similarly prestigious programs. But according to the authors of the new study, an early career researcher鈥檚 current working environment is a better predictor of their future success than is the prestige of their doctoral training.
鈥淧edigree is not destiny,鈥� says SFI External Professor (CU 麻豆影院), a co-author on the paper. 鈥淥ur analysis supports the fairly radical idea for academia that where you train doesn鈥檛 directly impact your future productivity.鈥�
The team looked at two basic measures of academic success 鈥� productivity (how many papers a researcher publishes) and prominence (how often their work is cited) 鈥� of 2453 tenure-track faculty in all 205 Ph.D.-granting computer science departments in the US and Canada during the five years before and five years following those individual鈥檚 first faculty appointment.
鈥淲e wanted to disentangle the impact of environment on productivity and prominence, and to isolate the effects of where someone trained versus where they went on to work as faculty,鈥� says lead author (CU 麻豆影院). 鈥淥n the prominence side, people do retain some benefit from having studied in a prestigious Ph.D. program. They continue to accumulate citations from their doctoral work.鈥�
But the prestige of the training program seems to play little role in how many papers researchers go on to produce once they begin their appointments in a new place. 鈥淪omeone like me, who trained at Colorado, and someone from MIT鈥� if we both end up at Stanford, our productivity will look the same,鈥� says Way.
The authors identify several possible mechanisms driving the increased productivity of faculty at more prestigious institutions. Selection criteria in hiring, expectations for high productivity once hired, and selective retention of productive faculty were all considered. 鈥淲e only find weak evidence for each,鈥� says Way. However, the prestige of the current work environment had a strong effect on productivity.
Identifying the underlying 鈥渇orces that tilt the scientific playing field in favor of some scientists over others,鈥� as Clauset says, is important for identifying and potentially correcting the systemic biases that may be limiting the production of scientific knowledge.
鈥溾€ur findings have direct implications for research on the science of science, which often assumes, implicitly if not explicitly, that meritocratic principles or mechanisms govern the production of knowledge,鈥� write the authors. 鈥淭heories and models that fail to account for the environmental mechanism identified here, and the more general causal effects of prestige on productivity and prominence, will thus be incomplete.鈥�
window.location.href = `https://santafe.edu/news-center/news/pedigree-not-destiny-when-it-comes-scholarly-success`;Musical tastes reflect our unique values and experiences, our relationships with others, and the places where we live. But as each of these things changes, do our tastes also change to reflect the present, or remain fixed, reflecting our past? Here, we investigate how where a person lives shapes their musical preferences, using geographic relocation to construct quasi-natural experiments that measure short- and long-term effects. Analyzing comprehensive data on over 16 million users on Spotify, we show that relocation within the United States has only a small impact on individuals鈥� tastes, which remain more similar to those of their past environments. We then show that the age gap between a person and the music they consume indicates that adolescence, and likely their environment during these years, shapes their lifelong musical tastes. Our results demonstrate the robustness of individuals鈥� musical identity, and shed new light on the development of preferences.
window.location.href = `https://arxiv.org/pdf/1904.04948.pdf`;
As Benjamin Franklin once joked, death and taxes are universal. Scale-free networks may not be, at least from CU 麻豆影院.
The research challenges a popular two-decade-old theory that networks of all kinds, from Facebook and Twitter to the interactions of genes in yeast cells, follow a common architecture that mathematicians call 鈥渟cale-free.鈥�
Such networks fit into a larger category of networks that are dominated by a few hubs with many more connections than the vast majority of nodes鈥攖hink Twitter where for every Justin Bieber (105 million followers) and Kim Kardashian (60 million followers) out there, you can find thousands of users with just a handful of fans.
Key takeawaysIn research published this week in the journal Nature Communications, CU 麻豆影院鈥檚 Anna Broido and Aaron Clauset set out to test that trendy theory. They used computational tools to analyze a huge dataset of more than 900 networks, with examples from the realms of biology, transportation, technology and more.
Their results suggest that death and taxes may not have much competition, at least in networks. Based on Broido and Clauset鈥檚 analysis, close to 50 percent of real networks didn鈥檛 meet even the most liberal definition of what makes a network scale-free.
Those findings matter, Broido said, because the shape of a network determines a lot about its properties, including how susceptible it is to targeted attacks or disease outbreaks.
鈥淚t鈥檚 important to be careful and precise in defining things like what it means to be a scale-free network,鈥� said Broido, a graduate student in the Department of Applied Mathematics.
Clauset, an associate professor in the Department of Computer Science and the BioFrontiers Institute, agrees.
鈥淭he idea of scale-free networks has been a unifying but controversial theme in network theory for nearly 20 years,鈥� he said. 鈥淩esolving the controversy has been difficult because we lacked good tools and broad data. What we鈥檝e found now is that there is little evidence for classically scale-free networks except in a few specific places. Most networks don鈥檛 look scale-free at all.鈥�
Deciding whether or not a network is 鈥渟cale-free,鈥� however, can be tricky. Many types of networks look similar from a distance.
But Scale-free networks are special because the patterns of connections coming into and out of nodes follows a precise mathematical form called a power law distribution.
鈥淚f human height followed a power law, you might expect one person to be as tall as the Empire State Building, 10,000 people to be as tall as a giraffe, and more than 150 million to be only about 7-inches-tall,鈥� Clauset said.
Beginning in the late 1990s, a handful of researchers made a bold claim that all real-world networks follow a universal structure represented by such giraffe- and inch-sized disparities.
There was just one problem: 鈥淭he original claims were mostly based on analyzing a handful of networks with very rough tools,鈥� Clauset said. 鈥淭he idea was provocative, but also, in retrospect, quite speculative.鈥�
To take scale-free networks out of the realm of speculation, he and Broido turned to the . This archive, which was assembled by Clauset鈥檚 research group at CU 麻豆影院, lists data on thousands of networks from every scientific domain. They include the social links between Star Wars characters, interactions among yeast proteins, friendships on Facebook and Twitter, airplane travel and more.
Their findings were stark. By applying a series of statistical tests of increasing severity, the researchers calculated that only about 4 percent of the networks they studied met the strictest criteria for being scale free, meaning the number of connections that each node carried followed a power-law distribution. These special networks included some types of protein networks in cells and certain kinds of technological networks.
But not all researchers use those exact requirements to decide what makes a scale-free network, Broido said. To account for these alternative definitions, she and Clauset adapted their tests to account for each of the variations.
鈥淲herever you鈥檙e coming from, one of our definitions should be close to what you鈥檙e thinking,鈥� Broido said.
Despite the added flexibility, most networks still failed to show evidence even for weakly scale-free structure. Roughly half of all biological networks and all social networks, for example, didn鈥檛 look like anything close to a scale-free network, no matter how flexible the definitions were made.
Far from being a let-down, Clauset sees these null findings in a positive light: if scale-free isn鈥檛 the norm, then scientists are free to explore new and more accurate structures for the networks people encounter every day.
鈥淭he diversity of real networks presents a mystery,鈥� he said. 鈥淲hat are the common shapes of the networks? How do different kinds of networks assemble and maintain their structure over time? I鈥檓 excited that our findings open up room to explore new ideas.鈥�