Wang, D., Song, C., & Barabási, A.-L. (2013). Quantifying Long-Term Scientific Impact. Science, 342(6154), 127 LP – 132. https://doi.org/10.1126/science.1237825

• Interesting, figure 1b shows that high citations after 30 years isn't well predicted by citation # after 2 years.
• Prediction of citation future is really hard -- (i.e. they did a pretty bad job)
• citations go down with age, peaking 2-3 years after birth. (they fit a lognormal)
• they fit their own custom model, which includes intrinsic worth, to early paper citation distributions to predict their future
• pretty devastating critique by another Wang et al. (2015):
  • Wang, J., Mei, Y., & Hicks, D. (2014). Comment on “Quantifying long-term scientific impact.” Science, 345(6193), 149 LP – 149. https://doi.org/10.1126/science.1248770
  • their model predicts essentially nothing, and doesn't do better than assuming the paper will get no more citations than it did at 5 years

Wallace, M. L., Larivière, V., & Gingras, Y. (2009). Modeling a century of citation distributions. Journal of Informetrics, 3(4), 296–303. https://doi.org/10.1016/j.joi.2009.03.010

(description of Web of Science data)
Data for this paper are drawn from Thomson Scientific’s Web of Science, including the Century of Science database, alongwith the Science Citation Index Expanded (SCIE) and the Social Sciences Citation Index (SSCI).

• the percent of works that go completely uncited (after 2&5 years) has dropped dramatically over the years. it increased briefly in 1960s (guessing because of increase in book reviews), but continued to decline after.

• still 40% of works go uncited the two years following publication, and 20% ten years following.

• "rapid exponential growth in publishing has ended since about the mid-seventies" (300)

• explains the drop in uncited papers as an increase in the number of citations [more publishing, more cites per pub]

• found a function which fits the degree distribution at all times, and all citation ranges (Tsallis). cool that it actually relates to a dynamical network model of some sort. It's a "q-exponential" degree distribution. It's called "non-extensive statistical mechanics". this is really cool!

• A mathematical theory of citing seems interesting. You pick up a random selection of recent papers, and cite them and some of their references. This produces a correct degree distribution

Moody and Light (2006) A View from Above: The Evolving Sociological Landscape

I'm going to do a deeper reading of this one.

• Intro:
  • Where does Sociology sit compared to other social sciences?
  • Sociology is central, but not internally cohesive
  • Move away from fundamental processes to social problems
  • Others:
    • References Abbott's "Silk road" of ideas, as an alternative model to Kuhnian revolution
      • Ideas ebb and flow with few dramatic fissures. Social science knowledge, to Friedrichs (1970), benefits from pluralistic debate.
    • Horowitz (1993): fragmentation (infiltration of "ideological" disciplines) has effaced foundation of sociology
  • No hegemonic cluster, porous boundaries (not so much fragmented)

Martin, J. L., & Lee, M. (2018). A formal approach to meaning. Poetics, 68, 10–17. https://doi.org/https://doi.org/10.1016/j.poetic.2018.01.002

• This article seems to be a key I can use to unlock meaning in my dataset. I just need to understand how it works.
• I propose a careful read of this one

Moody, J. (2004). The Structure of a Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999. American Sociological Review, 69(2), 213–238. https://doi.org/10.1177/000312240406900204

• Structural cohesion is important in the development of a science (cites Durkheim!?)
• Claims about consensus in Sociology lead to three distinct collaboration structures
  • Multiple disconnected research specialties (highly clustered)
  • Stars are important connectors unequal distribution of involvement in collaboration networks
  • Permeable theoretical boundaries and change in practice wide-ranging cross-cutting collaborations structurally cohesive network
• Gives a great theoretical overview, linking theory of theory to predictions about collaboration structure
• Quantitative methods is the main force driving increases in coauthorship
• Within journal: Proportion of papers with tables correlates with proportion coauthored
• The coauthorship network is not small-world
• Collaboration happens freely across disciplinary boundaries, and there is not high clustering

Edelmann, A. (2018). Formalizing symbolic boundaries. Poetics, 68, 120–130. https://doi.org/https://doi.org/10.1016/j.poetic.2018.04.006

• boundaries: "what kinds of behavior or opinions are appropriate for different kinds of people"
• dual-classifications of groups and behaviors, by their cooccurrence patterns, into cultural categories
• starts with a matrix of the approval associated with a certain group (row) engaging in some behavior (column)
  • this matrix can be relative to the perceiving group
  • "variance and entropy of the distribution reflect strength and salience of the symbolic boundary"
• huge theoretical lit review (and the rest of the paper) is on boundary-breaking embedded in social networks via cognitive science

Bertin, M., Atanassova, I., Sugimoto, C. R., & Lariviere, V. (2016). The linguistic patterns and rhetorical structure of citation context: an approach using n-grams. Scientometrics, 109(3), 1417–1434. https://doi.org/10.1007/s11192-016-2134-8

• look only at 3-grams which contain verbs
• trends of each verb (classes of n-grams) over progression following the IMRaD structure (%)
• also some examination of citation context (for negation)
• given the amazing data, the methods are pretty simplistic

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• Zhou, Y., Lin, H., Liu, Y., & Ding, W. (2019). A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry. Scientometrics, 120(1), 167–185. https://doi.org/10.1007/s11192-019-03126-8

Theory

Application

• [above] Wallace, M. L., Larivière, V., & Gingras, Y. (2009). Modeling a century of citation distributions. Journal of Informetrics, 3(4), 296–303. https://doi.org/10.1016/j.joi.2009.03.010
• Xiao, S., Yan, J., Li, C., Jin, B., Wang, X., Yang, X., … Zhu, H. (2016). On Modeling and Predicting Individual Paper Citation Count over Time. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2676–2682. Retrieved from http://dl.acm.org/citation.cfm?id=3060832.3060995
  • Doesn't seem worth my time. Interesting model, but dubious theoretical ideas. Maybe worth mentioning in this light?
• Also found the Microsoft academic graph dataset - update: for sociology it's super limited. might be useful for other fields (e.g. CS)

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