Science is a growing system, exhibiting ~4% annual growth in publications and ~1.8% annual growth in the number of references per publication. Combined these trends correspond to a 12-year doubling period in the total supply of references, thereby challenging traditional methods of evaluating scientific production, from researchers to institutions. Against this background, we analyzed a citation network comprised of 837 million references produced by 32.6 million publications over the period 1965-2012, allowing for a temporal analysis of the `attention economy' in science. Unlike previous studies, we analyzed the entire probability distribution of reference ages - the time difference between a citing and cited paper - thereby capturing previously overlooked trends. Over this half-century period we observe a narrowing range of attention - both classic and recent literature are being cited increasingly less, pointing to the important role of socio-technical processes. To better understand the impact of exponential growth on the underlying knowledge network we develop a network-based model, featuring the redirection of scientific attention via publications' reference lists, and validate the model against several empirical benchmarks. We then use the model to test the causal impact of real paradigm shifts, thereby providing guidance for science policy analysis. In particular, we show how perturbations to the growth rate of scientific output affects the reference age distribution and the functionality of the vast science citation network as an aid for the search & retrieval of knowledge. In order to account for the inflation of science, our study points to the need for a systemic overhaul of the counting methods used to evaluate citation impact - especially in the case of evaluating science careers, which can span several decades and thus several doubling periods.

Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist's future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their "predictive power". Moreover, the predictive power of these models depend heavily upon scientists' career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions.

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.