<!-- TITLE: Literature --> <!-- SUBTITLE: A detailed annotated bibliography of all relevant literature --> To write an annotated bibliography entry *please* stick to simple language. Don't use too many words. Skip irrelevant details. Skip the jargon. A good pace for creating bibliographies like this is a **minimum of 5 per day**. It's small enough that you can do it every day, and it's equivalent to a rate of more than 150 papers per month. # The master list ## Quantifying Long-Term Scientific Impact >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 :P ## Modeling a century of citation distributions >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](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.76.036111)). 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](https://doi.org/10.1002/asi.20653) 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 :P ## The Evolving Sociological Landscape >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) ## A formal approach to meaning > 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](/journal-analysis/literature/MartinLee2018) of this one ## Structure of a Social Science Collaboration Network >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 $\to$ unequal distribution of involvement in collaboration networks + Permeable theoretical boundaries and change in practice $\to$ wide-ranging cross-cutting collaborations $\to$ 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 ## Formalizing symbolic boundaries > 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 ## Citation contexts, n-gram counts, and rhetorical structure >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 # Extended bibliography ## Discourse analysis of essays + Stab, C., & Gurevych, I. (2016). Parsing Argumentation Structures in Persuasive Essays. (October 2012). https://doi.org/10.1162/COLI + Teufel, S., Carletta, J., & Moens, M. (1999). An annotation scheme for discourse-level argumentation in research articles. Proceedings of the Ninth Conference on European Chapter of the Association for Computational Linguistics -, 110. https://doi.org/10.3115/977035.977051 ## Meaning emerges through interaction + Cunliffe, A., & Coupland, C. (2012). From hero to villain to hero: Making experience sensible through embodied narrative sensemaking. Human Relations, 65(1), 63–88. https://doi.org/10.1177/0018726711424321 ## Sociology of knowledge + Baehr, Peter. 2016. Founders, Classics, Canons: Modern Disputes over the Origins and Appraisal of Sociology’s Heritage. Second. New Brunswick: Transaction Publishers. + Merton, R. K. (1972). Insiders and Outsiders: A Chapter in the Sociology of Knowledge. American Journal of Sociology, 78(1), 9–47. https://doi.org/10.1086/225294 + Zhou, Y., Dong, F., Kong, D., & Liu, Y. (2019). Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies. Technological Forecasting and Social Change, 144(May), 205–220. https://doi.org/10.1016/j.techfore.2019.03.014 + 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 ## Formal approaches to analyzing meaning (i.e. culture) ### Theory + Edelmann, A., & Mohr, J. W. (2018). Formal studies of culture: Issues, challenges, and current trends. Poetics, 68, 1–9. https://doi.org/https://doi.org/10.1016/j.poetic.2018.05.003 + Lee, M., & Martin, J. L. (2018). Doorway to the dharma of duality. Poetics, 68, 18–30. https://doi.org/https://doi.org/10.1016/j.poetic.2018.01.001 + 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 ### Application + Hoffman, M. A., Cointet, J.-P., Brandt, P., Key, N., & Bearman, P. (2018). The (Protestant) Bible, the (printed) sermon, and the word(s): The semantic structure of the Conformist and Dissenting Bible, 1660–1780. Poetics, 68, 89–103. https://doi.org/https://doi.org/10.1016/j.poetic.2017.11.002 + Lizardo, O. (2018). The mutual specification of genres and audiences: Reflective two-mode centralities in person-to-culture data. Poetics, 68, 52–71. https://doi.org/https://doi.org/10.1016/j.poetic.2018.04.003 ## How do citations accumulate over time? + [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](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/) dataset - update: for sociology it's super limited. might be useful for other fields (e.g. CS) ## Heap + DiMaggio, P., Sotoudeh, R., Goldberg, A., & Shepherd, H. (2018). Culture out of attitudes: Relationality, population heterogeneity and attitudes toward science and religion in the U.S. Poetics, 68, 31–51. https://doi.org/https://doi.org/10.1016/j.poetic.2017.11.001 + Abend, G. (2018). The Love of Neuroscience: A Sociological Account. Sociological Theory, 36(1), 88–116. https://doi.org/10.1177/0735275118759697 + Shapin, S. (1995). Here and Everywhere: Sociology of Scientific Knowledge. Annual Review of Sociology, 21(1), 289–321. https://doi.org/10.1146/annurev.soc.21.1.289 + Fine, G. A., & Kleinman, S. (1986). Interpreting the Sociological Classics: Can There be a “True” Meaning of Mead? Symbolic Interaction, 9(1), 129–146. https://doi.org/10.1525/si.1986.9.1.129 + Davis, M. S. (1986). “That’s Classic!” The Phenomenology and Rhetoric of Successful Social Theories. Philosophy of the Social Sciences, 16(3), 285–301. https://doi.org/10.1177/004839318601600301 + Seidman, S. (1983). Beyond Presentism and Historicism: Understanding the History of Social Science. Sociological Inquiry, 53(1), 79–91. https://doi.org/10.1111/j.1475-682X.1983.tb01167.x + Skinner, Q. (1972). Motives, Intentions and the Interpretation of Texts. New Literary History, 3(2), 393–408. https://doi.org/10.2307/468322 + Camic, C. (2003). Reconstructing the Theory of Action. Sociological Theory, 16(3), 283–291. https://doi.org/10.1111/0735-2751.00058 + Jones, R. A. (1977). On Understanding a Sociological Classic. American Journal of Sociology, 83(2), 279–319. https://doi.org/10.4324/9781315256627-18 + Lu, C., Ding, Y., & Zhang, C. (2017). Understanding the impact change of a highly cited article: a content-based citation analysis. Scientometrics, 112(2), 927–945. https://doi.org/10.1007/s11192-017-2398-7 + Cohan, A., Ammar, W., van Zuylen, M., & Cady, F. (2019). Structural Scaffolds for Citation Intent Classification in Scientific Publications. ArXiv Preprint, 1(1). Retrieved from http://arxiv.org/abs/1904.01608 + Boyack, K. W., Van Eck, N., Colavizza, G., & Waltman, L. (2018). Characterizing in-text citations in scientific articles: A large-scale analysis. Journal of Informetrics, 12(1), 59–73. Retrieved from http://10.0.3.248/j.joi.2017.11.005%0Ahttp://search.ebscohost.com/login.aspx?direct=true&db=llf&AN=128275234&site=ehost-live + Nakov, P. I., Schwartz, A. S., & Hearst, M. A. (2004). Citances: Citation Sentences for Semantic Analysis of Bioscience Text. Proceedings of the SIGIR’04 Workshop on Search and Discovery in Bioinformatics, 1–8. Retrieved from http://ai2-s2-pdfs.s3.amazonaws.com/0031/1d4a5ed649f720589a580c38d4035bfc65ea.pdf + Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018). Measuring the Evolution of a Scientific Field through Citation Frames. Transactions of the Association for Computational Linguistics, 6, 391–406. https://doi.org/10.1162/tacl_a_00028 + Teufel, S., Siddharthan, A., & Tidhar, D. (2010). Automatic classification of citation function. (July), 103. https://doi.org/10.3115/1610075.1610091 + Wuestman, M. L., Hoekman, J., & Frenken, K. (2019). The geography of scientific citations. Research Policy, 48(7), 1771–1780. https://doi.org/10.1016/j.respol.2019.04.004 + a location bias effect in citations is weak once topic similarity is controlled for + Enders, M., Havemann, F., & Jeschke, J. M. (2019). A citation-based map of concepts in invasion biology. NeoBiota, 47, 23–42. https://doi.org/10.3897/neobiota.47.32608 + Liu, J. S., Lu, L. Y. Y., & Ho, M. H.-C. (2019). A few notes on main path analysis. Scientometrics, 119(1), 379–391. https://doi.org/10.1007/s11192-019-03034-x + Hummon, N. P., & Carley, K. (1993). Social networks as normal science. Social Networks, 15(1), 71–106. https://doi.org/10.1016/0378-8733(93)90022-D + Hummon, N. P., & Doreian, P. (1989). Connectivity in a Citation Network: The Development of DNA Theory. Social Networks, 11, 39–63. + Flis, I., & van Eck, N. J. (2017). Framing Psychology as a Discipline (1950-1999): A large-scale term co-occurrence analysis of scientific literature in psychology. History of Psychology, 21(4), 1–58. https://doi.org/10.1037/hop0000067 + Adatto, K., & Cole, S. (1981). The Functions of Classical Theory in Contemporary Sociological Research: The Case of Max Weber. Knowledge and Society: Studies in the Sociology of Culture, 3, 137–162. + Ginsberg, M. (1930). The Concept of Evolution in Sociology. Proceedings of the Aristotlelian Societ, 31, 201–224. + Chubin, D. E., & Moitra, S. D. (1975). Content Analysis of References: Adjunt or Alternative to Citation Counting? Social Studies of Science, 5(4), 423–441. ## Popular extensions of "predict future citations" train of research + Clauset, A., Larremore, D. B., & Sinatra, R. (2017). Data-driven predictions in the science of science. Science, 355(6324), 477–480. https://doi.org/10.1126/science.aal4217 + Shen, H., Wang, D., Song, C., & Barabási, A. L. (2014). Modeling and predicting popularity dynamics via reinforced Poisson Processes. Proceedings of the National Conference on Artificial Intelligence, 1, 291–297. + Kuhn, T., Perc, M., & Helbing, D. (2014). Inheritance Patterns in Citation Networks Reveal Scientific Memes. Physical Review X, 4(4), 041036. https://doi.org/10.1103/PhysRevX.4.041036 + Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., … Barabási, A.-L. (2018). Science of science. Science, 359(6379), eaao0185. https://doi.org/10.1126/science.aao0185 + Ke, Q., Ferrara, E., Radicchi, F., & Flammini, A. (2015). Defining and identifying Sleeping Beauties in science. Proceedings of the National Academy of Sciences, 112(24), 7426–7431. https://doi.org/10.1073/pnas.1424329112 + Sinatra, R., Wang, D., Deville, P., Song, C., & Barabasi, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239–aaf5239. https://doi.org/10.1126/science.aaf5239 + Radicchi, F., & Castellano, C. (2015). Understanding the Scientific Enterprise: Citation Analysis, Data and Modeling BT - Social Phenomena: From Data Analysis to Models (B. Gonçalves & N. Perra, eds.). https://doi.org/10.1007/978-3-319-14011-7_8 + Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature, 489(7415), 201–202. https://doi.org/10.1038/489201a + Perc, M. (2014). The Matthew effect in empirical data. Journal of The Royal Society Interface, 11(98), 20140378. https://doi.org/10.1098/rsif.2014.0378 + 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 ## Interesting recent developments - soc of citations + Lin, C.-S. (2018). An analysis of citation functions in the humanities and social sciences research from the perspective of problematic citation analysis assumptions. Scientometrics, 116(2), 797–813. https://doi.org/10.1007/s11192-018-2770-2 + Pajić, D., Jevremov, T., & Škorić, M. (2019). Publication and citation patterns in the social sciences and humanities: A national perspective. Canadian Journal of Sociology, 44(1), 67–94. https://doi.org/10.29173/cjs29214 + Hyland, K., & Jiang, F. (Kevin). (2019). Points of Reference: Changing Patterns of Academic Citation. Applied Linguistics, 40(1), 64–85. https://doi.org/10.1093/applin/amx012 + Ridi, N. (2019). The Shape and Structure of the ‘Usable Past’: An Empirical Analysis of the Use of Precedent in International Adjudication. Journal of International Dispute Settlement, 10(2), 200–247. https://doi.org/10.31228/osf.io/g3ds8 + Tavares, O., Sin, C., & Lança, V. (2019). Inbreeding and Research Productivity Among Sociology PhD Holders in Portugal. Minerva, 57(3), 373–390. https://doi.org/10.1007/s11024-019-09378-1 + Meyer, M., Waldkirch, R. W., Duscher, I., & Just, A. (2018). Drivers of citations: An analysis of publications in “top” accounting journals. Critical Perspectives on Accounting, 51, 24–46. https://doi.org/10.1016/j.cpa.2017.07.001 + Kristensen, P. M. (2018). International Relations at the End: A Sociological Autopsy. International Studies Quarterly. https://doi.org/10.1093/isq/sqy002 + Wang, B., Bu, Y., & Xu, Y. (2018). A quantitative exploration on reasons for citing articles from the perspective of cited authors. Scientometrics, 116(2), 675–687. https://doi.org/10.1007/s11192-018-2787-6 + Korom, P. (2018). Does scientific eminence endure? Making sense of the most cited economists, psychologists and sociologists in textbooks (1970–2010). Scientometrics, 116(2), 909–939. https://doi.org/10.1007/s11192-018-2781-z ## Merton said a lot on this stuff... + Merton, R. K. (1957). Priorities in Scientific Discovery: A Chapter in the Sociology of Science. *American Sociological Review*, 22(6), 635. https://doi.org/10.2307/2089193 + Merton, R. K. (1968). Part IV: Studies in the Sociology of Science, in *Social Theory and Social Structure*. New York: The Free Press. + Merton, R. K. (1968). The Matthew Effect in Science. *Science*, 159(3810), 56–62. + Merton, R. K. (1972). Insiders and Outsiders: A Chapter in the Sociology of Knowledge. *American Journal of Sociology*, 78(1), 9–47. https://doi.org/10.1086/225294 + Merton, R. K. (1988). The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property. *Isis*, 79(4), 606–623. https://doi.org/10.1086/354848 ## Extracting bibliographies, comparing to WOS + van Eck, N. J., & Waltman, L. (2019). Accuracy of citation data in Web of Science and Scopus. Retrieved from http://arxiv.org/abs/1906.07011 + García-Pérez, M. A. (2010). Accuracy and completeness of publication and citation records in the Web of Science, PsycINFO, and Google Scholar: A case study for the computation of h indices in Psychology. Journal of the American Society for Information Science and Technology, 61(10), 2070–2085. https://doi.org/10.1002/asi.21372 + Olensky, M., Schmidt, M., & van Eck, N. J. (2016). Evaluation of the citation matching algorithms of CWTS and iFQ in comparison to the Web of science. Journal of the Association for Information Science and Technology, 67(10), 2550–2564. https://doi.org/10.1002/asi.23590