Crafty landmarks

The previous February week before the publication of this issue turned out to be fruitful for memorable dates and events in the history of science, birthdays of famous scientists...

The week began, of course, on Russian Science Day. This date was chosen because on February 8, 1724, by decree of the Senate, by order of Peter I, the Academy of Sciences was founded in Russia. Some materials dedicated to this great holiday decorated our room. It opens (and this is also, in a sense, an experiment!) with the publication of one of our readers. Quite a popular science publication from the “It would be funny if it weren’t so sad” series.

The aging of Russian science has become the talk of the town. Rescue measures are not only debated, but also cast into quite visible target indicators of strategies and programs. Using specific examples, we will show how the illusion of “radical” solutions to a given problem can be born.

Consider a community of scientists of 56 thousand, 42 thousand of whom are under 60 years of age (data close to real). Let's assume that we want to significantly rejuvenate it in 10 years, replacing 70-year-olds with 25-year-olds at a rate of a thousand a year. With such a replacement, we get a total rejuvenation of 450 thousand “age units” in total. However, 42 thousand people will age 10 years during this time; therefore, we must put 420 thousand “age units” on the opposite side of the scale. But that’s not all: the youth admitted at the rate of a thousand a year will also consistently age, which will result in an additional 55 thousand “age units” to the same cap. As a result, the average age of the community not only did not decrease, but even slightly increased. It would seem that such a favorable scheme for radical rejuvenation - but no! To be fair, we note: a slight variation in the average age towards reduction is still possible (for example, due to personnel mobility or due to the fact that if there is a shortage of 70-year-olds to replace our scheme, we would have to take people older and then younger than this age) , but this does not change the essence of the matter.

But how can we rejuvenate the research workforce in 10 years, say, from an average of 49 to 40 years old, as proposed by the draft strategy “Innovative Russia 2020” [1]? Let's consider the following scenario: let's leave the existing contingent alone and begin to grow a new one, recruiting N researchers aged 25 every year for 10 years. During this time, the old contingent will self-reduce (even to 46 thousand) and age (say, up to 56 years on average), and the new one will amount to 10N. Knowing the average age of the combined contingent at the end of the period (40 years), from a simple equation we obtain N = 7747 (people). As we can see, the goal set is utopian, like Manilov’s desire to reduce the average age of RAS scientists by 10-15 years by the end of the decade by annually admitting 5 thousand young candidates and doctors of science [2]. If we scale our calculation to the actual number of researchers in Russia (376 thousand in 2008), then the influx of “fresh blood” required for rejuvenation will become completely prohibitive: about 52 thousand people annually.

Where is the dog buried? The fact is that the average age indicator in a large personnel system is quite conservative. An inexperienced forecaster subconsciously does not take into account that everyone remaining in the system becomes a year older every year, and the newly admitted youth immediately begin to age. Despite all the conventions, the examples given clearly show the logical pitfalls for such forecasters. It is clear that such “schemes” of rejuvenation are unrealistic from any point of view. But even if we assume that they came true (after all, this is what the program compilers want), then there is little good here either: the resulting community will turn out to be unproductive. In science, the “harmony” of ages is still important. For sane forecast calculations of the structure and dynamics of scientific personnel systems, adequate mathematical models are required. Such models, for example, the evolution of the scientific community, structured by age and productivity, are being developed at CEMI RAS.

The personnel targets in the “Program for the Development of Nanoindustry in the Russian Federation until 2015” are tear-jerking [3]. According to its revised project, the share of people under 39 years of age among researchers in the field of nanoindustry should reach 50% by 2015 (the actual share in 2008 was 31%). Note that, with a significant student cohort, the median age of participants in RFBR nanoprojects in 2008 was 43.5 years [4]. The programmed number of researchers should increase from 10.3 in 2008 to 12.4 thousand people in 2015. According to a simple calculation, in order to achieve the target 50%, the first group (≤39 years old) needs to admit 300 people annually to expand the total contingent and approximately the same amount due to the dismissal of people from the second group (>39 years). Of course, this scheme would need to be supplemented by the natural decline and mobility of scientists, which, by the way, is easy to do in a mathematical model. However, in any case, such a frame reproduction mode seems practically impossible, including for the reasons discussed in [5]. This is a reduction in the number of students graduating since 2012 due to the fall in the birth rate in the 1990s, and the unpreparedness of educational and certification systems for the mass training of qualified specialists for interdisciplinary research, etc.

In conclusion, let us refer to historical experience. After the Great Patriotic War, the average age of scientific workers at the Academy of Sciences decreased only in the 1950s (recall that the Siberian Branch was formed in 1957), but in subsequent decades it grew: 1950 - 41.5; 1960 -38.3; 1970 - 38.5; 1980 - 41.3; 1990 - 43.2; 1998 - 47.9; 2008 - 51 years old. If the government does not take some extravagant steps (by declaring, for example, senior-year university students as researchers), then our research community will continue to age, which does not, however, exclude local successes. In order to avoid embarrassing situations, it is better to abandon target indicators for personnel altogether if the foresight specialists developing the programs are not good at math.

Alexander Terekhov,
Ph.D. physics and mathematics sciences,
leading researcher at CEMI RAS

1. www.economy/gov.ru/minec/activity/sections/innovations/doc20101231_016

2. RAS: truth and speculation ( www.strf.ru/organization.aspx?CatalogId=221&d_ no=28957 )

3. www.economy/gov.ru/minec/activity/sections/innovations/doc20101231_016

4. www.portalnano.ru/read/documents/met/mon-sm-538_16_16072010/pro-gram_2015

5. Terekhov A.I. On the human resource for nanotechnology development // 2010. Vol. 5, No. 5-6. P.7-10.