On the time dependence of the hh-index

The time dependence of the $h$-index is analyzed by considering the average behaviour of $h$ as a function of the academic age $A_A$ for about 1400 Italian physicists, with career lengths spanning from 3 to 46 years. The individual $h$-index is strongly correlated with the square root of the total citations $N_C$: $h \approx 0.53 \sqrt{N_C}$. For academic ages ranging from 12 to 24 years, the distribution of the time scaled index $h/\sqrt{A_A}$ is approximately time-independent and it is well described by the Gompertz function. The time scaled index $h/\sqrt{A_A}$ has an average approximately equal to 3.8 and a standard deviation approximately equal to 1.6. Finally, the time scaled index $h/\sqrt{A_A}$ appears to be strongly correlated with the contemporary $h$-index $h_c$

Persistence and Uncertainty in the Academic Career

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Since knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production n_{i}(t) of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. We compare our results with 21,156 sports careers. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly right-skewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist's collaboration radius and team efficiency.Comment: 29 pages total: 8 main manuscript + 4 figs, 21 SI text + fig

Hot Streaks on Social Media

Measuring the impact and success of human performance is common in various disciplines, including art, science, and sports. Quantifying impact also plays a key role on social media, where impact is usually defined as the reach of a user's content as captured by metrics such as the number of views, likes, retweets, or shares. In this paper, we study entire careers of Twitter users to understand properties of impact. We show that user impact tends to have certain characteristics: First, impact is clustered in time, such that the most impactful tweets of a user appear close to each other. Second, users commonly have 'hot streaks' of impact, i.e., extended periods of high-impact tweets. Third, impact tends to gradually build up before, and fall off after, a user's most impactful tweet. We attempt to explain these characteristics using various properties measured on social media, including the user's network, content, activity, and experience, and find that changes in impact are associated with significant changes in these properties. Our findings open interesting avenues for future research on virality and influence on social media.Comment: Accepted as a full paper at ICWSM 2019. Please cite the ICWSM versio

Modeling Collaboration in Academia: A Game Theoretic Approach

In this work, we aim to understand the mechanisms driving academic collaboration. We begin by building a model for how researchers split their effort between multiple papers, and how collaboration affects the number of citations a paper receives, supported by observations from a large real-world publication and citation dataset, which we call the h-Reinvestment model. Using tools from the field of Game Theory, we study researchers' collaborative behavior over time under this model, with the premise that each researcher wants to maximize his or her academic success. We find analytically that there is a strong incentive to collaborate rather than work in isolation, and that studying collaborative behavior through a game-theoretic lens is a promising approach to help us better understand the nature and dynamics of academic collaboration.Comment: Presented at the 1st WWW Workshop on Big Scholarly Data (2014). 6 pages, 5 figure

A review of the characteristics of 108 author-level bibliometric indicators

An increasing demand for bibliometric assessment of individuals has led to a growth of new bibliometric indicators as well as new variants or combinations of established ones. The aim of this review is to contribute with objective facts about the usefulness of bibliometric indicators of the effects of publication activity at the individual level. This paper reviews 108 indicators that can potentially be used to measure performance on the individual author level, and examines the complexity of their calculations in relation to what they are supposed to reflect and ease of end-user application.Comment: to be published in Scientometrics, 201

Interpreting Performance in Small Business Research

For obvious reasons, researchers and policy-makers alike have an interest in assessing the performance of small firms as well as in understanding the factors that contribute to it. Attaining such knowledge is not a trivial undertaking. Researchers have pointed out that the performance of small firms can be difficult to assess (Brush & Vanderwerf, 1992)—e.g., because reliable data cannot be obtained—and also difficult to predict (Cooper, 1995). In this paper I will discuss the equally important and difficult issue of how research results regarding small business performance and its predictors can or should be interpreted. In particular, I will discuss whether commonly used performance indicators like survival vs. non-survival and growth vs. non-growth really reflect ‘good’ vs. ‘bad’ performance, as is commonly assumed. Although theory and other researchers’ findings will also be used to some extent, my exposition will rely primarily on experiences and illustrations from a number of research projects I have been directly involved in during the last 20 years. The paper proceeds as follows. I will first question the assumption that business discontinuance—often called ‘failure’—is a ‘bad’ outcome that best should be avoided from the aggregate perspective of the economic system. I will then continue to discuss ‘failure’ from more of a micro-perspective, arguing that most instances of discontinuation of new or emerging firms are not associated with substantial financial losses and do not necessarily represent efforts that should have been avoided. Staying at the micro level I will then turn to the issue of firm growth and the conditions under which growth represents a ‘good’ outcome from the perspective of the firm’s principal stakeholders. I will then return to the aggregate level and discuss the extent to which firm level employment growth translates to net increases of employment in the economy. Finally, the implications of the issues raised in the paper will be restated and discussed in the concluding section of the paper

Game of Tenure: the role of “hidden” citations on researchers’ ranking in Ecology

Field ecologists and macroecologists often compete for the same grants and academic positions, with the former producing primary data that the latter generally use for model parameterization. Primary data are usually cited only in the supplementary materials, thereby not counting formally as citations, creating a system where field ecologists are routinely under-acknowledged and possibly disadvantaged in the race for funding and positions. Here, we explored how the performance of authors producing novel ecological data would change if all the citations to their work would be accounted for by bibliometric indicators. We collected the track record of >2300 authors from Google Scholar and citation data from 600 papers published in 40 ecology journals, including field-based, conservation, general ecology, and macroecology studies. Then we parameterized a simulation that mimics the current publishing system for ecologists and assessed author rankings based on number of citations, H-Index, Impact Factor, and number of publications under a scenario where supplementary citations count. We found weak evidence for field ecologists being lower ranked than macroecologists or general ecologists, with publication rate being the main predictor of author performance. Current ranking dynamics were largely unaffected by supplementary citations as they are 10 times less than the number of main text citations. This is further exacerbated by the common practice of citing datasets assembled by previous research or data papers instead of the original articles. While accounting for supplementary citations does not appear to offer a solution, researcher performance evaluations should include criteria that better capture authors’ contribution of new, publicly available data. This could encourage field ecologists to collect and store new data in a systematic manner, thereby mitigating the data patchiness and bias in macroecology studies, and further accelerating the advancement of ecology and related areas of biogeography

The dynamics and inequality of Italian male earnings: permanent changes or transitory fluctuations?

This paper looks at longitudinal aspects of changes in Italian male earnings inequality since the late 1970s by decomposing the earnings autocovariance structure into its persistent and transitory parts. Cross-sectional earnings differentials are found to grow over the period. The longitudinal analysis shows that such growth is determined by the permanent earnings component and is due both to a divergence of earnings profiles over the working career and an increase in overall persistence during the first half of the 1990s. Using these estimates to analyse low pay probabilities shows that it became more persistent for all birth cohorts; consequently, the probability of repeated low pay episodes also increased during the sample period. When allowing for occupation-specific components in the parameters of interest, life time earnings divergence is found to characterise the non-manual earnings distribution.Earnings Inequality, Earnings Dynamics, Minimum Distance Estimation

Career Transitions and Trajectories: A Case Study in Computing

From artificial intelligence to network security to hardware design, it is well-known that computing research drives many important technological and societal advancements. However, less is known about the long-term career paths of the people behind these innovations. What do their careers reveal about the evolution of computing research? Which institutions were and are the most important in this field, and for what reasons? Can insights into computing career trajectories help predict employer retention? In this paper we analyze several decades of post-PhD computing careers using a large new dataset rich with professional information, and propose a versatile career network model, R^3, that captures temporal career dynamics. With R^3 we track important organizations in computing research history, analyze career movement between industry, academia, and government, and build a powerful predictive model for individual career transitions. Our study, the first of its kind, is a starting point for understanding computing research careers, and may inform employer recruitment and retention mechanisms at a time when the demand for specialized computational expertise far exceeds supply.Comment: To appear in KDD 201