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I'm interested in the birth / death / life cycle of scientific fields over time, and looking for quantitative metrics that suggest whether a particular scientific field is in decline. A simple and common metric (from the field of bibliometrics) is the number of publications in a given time period, but I'd like to delve deeper: e.g., consider to what degree researchers in a subfield are building on each others' work, versus pursuing independent and idiosyncratic lines of inquiry that don't materially engage with others. (I'm focusing on fields in social science, in case the context matters.) I have two specific questions:

  1. Suppose I have a database of publications and citations for a particular subfield over a given time period. What network measure would allow me to show how cohesive the field is, as measured by the citation network? (My best guess is the distribution of the cardinality of connected components; are there other metrics to consider?)

  2. What other papers should I read, that have measured the decline and death of scientific fields? I'm not the first person to look for metrics on the rise and decline of scientific fields – but all the papers I've found (e.g., this one) focus mainly on the number of publications in a time period. This seems well suited for establishing that a subfield is well on the way to death, but less good for measuring 'warning signs', e.g., in a field that may be in declining health even as the number of papers remains roughly constant. Are there papers that have used network or bibliometric measures to look for signs of decline, using metrics other than sheer number of publications?

If there is another forum/community that matches the question better, please let me know.

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  • $\begingroup$ I seem to recall that Web of Science published something on this ... en.wikipedia.org/wiki/Web_of_Science That was many years ago, so i no longer remember much about it. $\endgroup$
    – Gerald Edgar
    Aug 23, 2018 at 13:38
  • $\begingroup$ if I have to make that assessment, I will start with the best journal of that field and trace the annual reviews in that specific field- and i am definite i can get a database on the growth and decay of the research fields say in nuclear science. $\endgroup$
    – drvrm
    Aug 24, 2018 at 16:30
  • $\begingroup$ The question appears to be off-topic: it does not appear to be about the history of any science or mathematics or of bibliometrics itself, it seems rather to aim at creating a discussion about generating new bibliometric techniques. Hence my 'close' vote. I'd like to be able to suggest an alternative forum but I'm not familiar with any. $\endgroup$
    – terry-s
    Sep 25, 2018 at 9:48
  • $\begingroup$ @terry-s I agree this question is unusual for this forum, thus the comment in my final paragraph, but I looked for alternative communities and concluded this was the best fit. I remain open to other thoughts and recommendations. $\endgroup$
    – herfa
    Oct 8, 2018 at 19:40
  • $\begingroup$ For a certain scale of analysis, presence of field descriptors in catalogues of research categorisation could be a binary quantitative measure of decline as “termination.” Share of competitive reasearch grants (eg Australian Research Council) categorised as field is a size of pie issue, but %GDP inflation adjusted share would give an idea of the “competitiveness” of the field against ranking bodies. $\endgroup$
    – Samuel Russell
    Oct 27, 2018 at 6:43

2 Answers 2

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While there is not likely to be a single source which addresses your query, direction and suggestions may be had from several differing sources:

  • Machlup's 1962 The Production And Distribution Of Knowledge In The United States focuses on the patent system but expands widely beyond that. https://www.google.com/books/edition/The_Production_and_Distribution_of_Knowl/kp6vswpmpjoC?hl=en&gbpv=0

  • A global decline in research productivity? Evidence from China and Germany https://www.sciencedirect.com/science/article/abs/pii/S0165176520304067#! In a recent paper, Bloom et al. (2020) find evidence for a substantial decline in research productivity in the U.S. economy during the last 40 years. In this paper, we replicate their findings for China and Germany, using detailed firm-level data spanning three decades. Our results indicate that diminishing returns in idea production are a global phenomenon, not just confined to the U.S.

  • Cauwels and Sornette, Are ‘Flow of Ideas’ and ‘Research Productivity’ in secular decline? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3716939 It is widely held true that fundamental scientific knowledge has been accelerating exponentially over the past centuries and will continue to do so for the foreseeable future. Moreover, endogenous growth theory postulates that this exponential accumulation of knowledge is the main source of the ubiquitous exponential economic growth. We test these claims by constructing two new series of knowledge indices, one representing the historical evolution of the Flow of Ideas, the other of the Research Productivity, for the time period between 1750 and 1988. Three different geographical regions are covered: 1) Continental Europe, 2) the United Kingdom, and 3) the United States; and two disciplines: a) the physical sciences, and b) the life sciences. Our main result is that scientific knowledge has been in clear secular decline since the early 1970s for the Flow of Ideas and since the early 1950s for the Research Productivity. We also observe waves coinciding with the three industrial and technological revolutions, in particular in the United Kingdom. Overall, our results support the Kuhnian theory of knowledge creation through scientific revolutions, punctuation and paradigm shifts and falsify the gradualism that lies at the basis of the currently prevailing economic paradigm of endogenous growth.

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  1. I would look for measures of robusteness of (connected components of) the network. The number of connected components itself is probably a very rough measure. What happens if you randomly delete a small number of knots from your network? How much properties of the whole network are shared with large subnetworks?
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