Abstract
Many collective human activities have been shown to exhibit universal
patterns. However, the possibility of universal patterns across timing events
of researcher migration has barely been explored at global scale. Here, we show
that timing events of migration within different countries exhibit remarkable
similarities. Specifically, we look at the distribution governing the data of
researcher migration inferred from the web. Compiling the data in itself
represents a significant advance in the field of quantitative analysis of
migration patterns. Official and commercial records are often access
restricted, incompatible between countries, and especially not registered
across researchers. Instead, we introduce GeoDBLP where we propagate
geographical seed locations retrieved from the web across the DBLP database of
1,080,958 authors and 1,894,758 papers. But perhaps more important is that we
are able to find statistical patterns and create models that explain the
migration of researchers. For instance, we show that the science job market can
be treated as a Poisson process with individual propensities to migrate
following a log-normal distribution over the researcher's career stage. That
is, although jobs enter the market constantly, researchers are generally not
"memoryless" but have to care greatly about their next move. The propensity to
make k>1 migrations, however, follows a gamma distribution suggesting that
migration at later career stages is "memoryless". This aligns well but actually
goes beyond scientometric models typically postulated based on small case
studies. On a very large, transnational scale, we establish the first general
regularities that should have major implications on strategies for education
and research worldwide.
Description
[1304.7984] GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science
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