Notes on papers I read this week (I)

Cañibano, C. & Bozeman, B. Curriculum vitae method in science policy and research evaluation: the state-of-the-art. Research Evaluation, 18(2), 86-94

This paper reviews the use of CV analysis in science policy. The value of CVs lies in the fact that they serve as personal services advertisement and the fact that researchers are strongly encouraged to provide timely and accurate data. Until early 1990s CV analysis has been used anecdotally and as complementary. However, the Research Value Mapping programme developed by Bozeman and Rogers among others, has fostered its expansion as a solid methodology. Contrarily to other methodological approaches, CV analysis is characterized by being theory-driven. There are three main research topics in which this method has been applied: Career trajectories, mobility and mapping collective capacity. However, CV analysis is not free of many methodological limitations, namely: availability, heterogeneity, truncation, missing information, and coding inconsistency. They suggest solving part of this problems by complementing the data with other sources such as bibliometric data or survey data.

Dietz, J.S. & Bozeman, B. (2005). Academic careers, patents, and productivity: industry experience as scientific and technical human capital. Research Policy, 34(2), 349-367

This paper intends to analyze productivity differences based on career paths of scientists within industry, government and academia who have ended up in academia. The paper is framed within Bozeman’s STHC framework. They argue that most studies have focused until then either in industry or academia and few on the collaboration patterns between academia and industry, but always considering researchers as either academic or industry, instead of acknowledge the diversity of career patterns observed in their trajectories. One of the arguments made is that by favoring capacity (in this case seen as richness in career trajectories) one favors knowledge production. Hence their first hypothesis is that those with more diversified career patterns will be more productive and confront it to another hypothesis which states that scientist who always worked in academia will be more productive. While the former is based on social capital grounds (more ties, more connections, more productivity), the latter is based on job priorization and incentives, as publication is one of the main tasks of scientists. Two alternative hypotheses are also formulated: 1) early career experiences in academia will lead to more productivity, and 2) publishing before PhD will also warrant being more productive in the future. They observe that precocity and homogeneity in career patterns has a weak positive relation with productivity while years in industry and time of PhD. They compared productivity means between those groups who moved from industry to academia and viceversa, before and after the moving and observed increased productivity in movements. While the framing of the paper is really strong and inspiring, its results are not sufficiently convincing.

Interesting points:

  • Homogeny variable. They ‘quantify’ careers based on how distant they are from the norm based on the probability of a given trajectory being similar to the always academic one.
  • Education and traning precocity. Based on PhD year and whether they had academic experience soon in their career and if they published before PhD.


Cover photograph: Workers of the world, unite! at

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