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Integrating human and machine coding to measure political issues in ethnic newspaper articles

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Abstract

The voices of racial minority groups have rarely been examined systematically with large-scale text analysis in political science. This study fills such a gap by applying an integrated classification framework to the analysis of the commonalities and differences in political issues that appeared in 78,305 articles from Asian American and African American newspapers from the 1960s to the 1980s. The automated text classification shows that Asian American newspapers focused on promoting collective gains more often than African American newspapers. Conversely, African American newspapers concentrated on preventing collective losses more than Asian American newspapers. The content analysis demonstrates that the issue priorities varied between the corpora, especially with respect to policy contexts. Gaining access to government resources was a more urgent issue for Asian Americans, while reducing or ending state violence, such as police brutality, was a more pressing matter for African Americans. It also helped avoid extreme interpretations of the machine coding, as the misalignment of political agendas between the two corpora widened up to 10 times when the training data were measured using the minimum, rather than the maximum, reliability threshold.

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Data availability

Not available due to copyright restrictions.

Code availability

All replication files can be found at https://github.com/jaeyk/content-analysis-for-evaluating-ML-performances.

Notes

  1. For more information, see https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/163.

  2. For more information, see https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/6841.

  3. For more information, see https://www.pewresearch.org/topics/national-survey-of-latinos/.

  4. For more information, see https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/3832.

  5. For more information, see https://naasurvey.com/data/.

  6. For more information, see https://cmpsurvey.org/.

  7. For more information, see https://www.proquest.com/products-services/ethnic_newswatch.html.

  8. Greaves, Kay, “Davis Bail is Canceled; Poindexter Out on Bail,” Oakland Post, October 22, 1970: 13.

  9. Chin, Karen, ”Pacific/Asian Elderly Conference: Social Service Providers Must Get Their ’Act Together’,” International Examiner, April 30, 1979.

  10. Fleming, Thomas,“Thomas Fleming’s Weekly Report,” Sun Reporter, August 2, 1975:7

  11. Berling, Lynn, “‘Post’ Tries to be Only Daily for Black Community’,’ Oakland Post, February 15, 1981: 6.

  12. For more information, see https://www.proquest.com/products-services/ethnic_newswatch.html.

  13. Several studies have demonstrated how this measure tends to overestimate the true agreement among human coders [8, 69, 102].

  14. In practice, a kappa smaller than or equal to 0 indicates no agreement, a kappa in the 0.01–0.02 range indicates slight agreement, a kappa in the 0.21–0.40 range indicates fair agreement, a kappa in the 0.41–0.60 range indicates moderate agreement, a kappa in the 0.61–0.80 range indicates substantial agreement, and a kappa in the 0.81–1 range indicates an almost perfect agreement ([76], 279).

  15. I only used the top 5000 most frequently appearing terms because Zipf’s law expects frequently appearing features in documents to be a small fraction [118, 119]. The rest of the features will only increase sparsity in the training data and slow down the algorithmic process.

  16. Anonymous, “Cairo, Illinois: From Exploitation To Freedom,” Sun Reporter, March 27, 1971: 8.

  17. Iwamoto, Gary, “A Picture of the 70’s,” International Examiner, December 31, 1979: 8.

References

  1. Alexander, M. (2012). The New Jim Crow: Mass incarceration in the age of colorblindness. New York: The New Press.

    Google Scholar 

  2. Bailey, M. J., & Danziger, S. (2013). Legacies of the war on poverty. New York: Russell Sage Foundation.

    Google Scholar 

  3. Barberá, P., Boydstun, A.E., Linn, S., McMahon, R., & Nagler, J. (2019). “Automated text classification of news articles: a practical guide.” Political Analysis: 1–24.

  4. Bartels, L. M. (1999). Panel effects in the American National election studies. Political Analysis, 8(1), 1–20.

    Article  Google Scholar 

  5. Bender, E. M., & Friedman, B. (2018). Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6, 587–604.

    Article  Google Scholar 

  6. Berelson, B. (1952). Content analysis in communication research. Free press.

  7. Beretta, E., Vetrò, A., Lepri, B., & De Martin, J.C. (2018). “Ethical and Socially-Aware Data Labels.” In Annual International Symposium on Information Management and Big Data, 320–327. Springer.

  8. Birkimer, J. C., & Brown, J. H. (1979). Back to basics: Percentage agreement measures areaAdequate, but there are Easier W ays. Journal of Applied Behavior Analysis, 12(4), 535–543.

    Article  Google Scholar 

  9. Brady, H. E. (2019). The challenge of big data and data science. Annual Review of Political Science, 22, 297–323.

    Article  Google Scholar 

  10. Breiman, L. (1997). Arcing the Edge. Technical report. Technical Report 486, Statistics Department, University of California, Berkeley.

  11. Brilliant, M. (2010). The color of America has changed: How Racial Diversity shaped civil rights reform in California, 1941–1978. Oxford: Oxford University Press.

    Google Scholar 

  12. Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M. (2010). The Balanced Accuracy and Its Posterior Distribution. In 2010 20th International Conference on Pattern Recognition, 3121–3124. IEEE.

  13. Brooks, C. (2009). Alien neighbors, foreign friends: Asian Americans, housing, and the transformation of Urban California. Chicago: University of Chicago Press.

    Book  Google Scholar 

  14. Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1980). The American voter. Chicago: University of Chicago Press.

    Google Scholar 

  15. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the Multitrait-multimethod Matrix. Psychological Bulletin, 56(2), 81.

    Article  Google Scholar 

  16. Chae, D. H., Takeuchi, D. T., Barbeau, E. M., Bennett, G. G., Lindsey, J., & Krieger, N. (2008). Unfair treatment, racial/ethnic discrimination, ethnic identification, and smoking among Asian Americans in the national Latino and Asian American Study. American Journal of Public Health, 98(3), 485–492.

    Article  Google Scholar 

  17. Chan, A. B. (1983). Gold mountain: The Chinese in the new world. Vancouver: New Star Books.

    Google Scholar 

  18. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In KDD ’16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

  19. Chin, D. (2001). Seattle’s international district: The making of a Pan-Asian American community. Washington: University of Washington Press.

    Google Scholar 

  20. Chin, G. (2015). Building community, Chinatown style: a half century of leadership in San Francisco Chinatown. San Francisco: Friends of Chinatown Community Development Center.

    Google Scholar 

  21. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.

    Article  Google Scholar 

  22. Covin, D. (2009). Black politics after the civil rights movement: Activity and beliefs in sacramento, 1970–2000. : McFarland.

  23. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281.

    Article  Google Scholar 

  24. Danziger, S., & Haveman, R. (1981). The Reagan budget: A sharp break with the past. Challenge, 24(2), 5–13.

    Article  Google Scholar 

  25. Dawson, M. (1994a). A Black Counterpublic?: Economic earthquakes, racial agendas, and black politics. Public Culture, 7(1), 195–223.

    Article  Google Scholar 

  26. Dawson, M. (1994b). Behind the Mule: Race and class in African–American politics. Princeton: Princeton University Press.

    Google Scholar 

  27. Dawson, M. (2001). Black visions: The roots of contemporary African–American political ideologies. Chicago: University of Chicago Press.

    Google Scholar 

  28. Denny, M., & Spirling, A. (2017). Text preprocessing for unsupervised learning: Why it matters, When it misleads, and What to do about it.Political Analysis.

  29. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pretraining of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805.

  30. Elish, M. C., & Boyd, D. (2018). Situating methods in the magic of big data and AI. Communication Monographs, 85(1), 57–80.

    Article  Google Scholar 

  31. Espiritu, L. Y. (1992). Asian American panethnicity: Bridging institutions and identities. Philadelphia: Temple University Press.

    Google Scholar 

  32. Fraga, L. R., Garcia, J. A., Hero, R. E., Jones-Correa, M., Martinez-Ebers, V., & Segura, G. M. (2011). Latinos in thenew millennium: An almanac of opinion, behavior, and policy rreferences. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  33. Freund, Y., & Schapire, R. (1999). A short introduction to boosting. Journal of Japanese Society For Artificial Intelligence, 14(771–780), 1612.

    Google Scholar 

  34. Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics: 1189–1232.

  35. Friedman, J., Hastie, T., Tibshirani, R., et al. (2000). Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 28(2), 337–407.

    Article  Google Scholar 

  36. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J.W., Wallach, H., Hal Daumé III, & Crawford, K. (2018). Datasheets for datasets. arXiv preprint arXiv:1803.09010.

  37. Geiger, R.S., Yu, K., Yang, Y., Dai, M., Qiu, J., Tang, R., Huang, J. (2020). Garbage in, Garbage out? Do machine learning application papers in social computing report where human-labeled training data comes from?” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 325–336.

  38. Gitelman, L. (Ed.). (2013). Raw data. Is an Oxymoron: MIT press.

  39. Goth, G. (2016). Deep or shallow, NLP is breaking out. Communications of the ACM.

  40. Gottschalk, M. (2016). Caught: The Prison State and the lockdown of American Politics. Princeton: Princeton University Press.

    Book  Google Scholar 

  41. Grimmer, J., Messing, S., & Westwood, S. J. (2012). How words and money cultivate a personal vote: The effect of legislator credit claiming on constituent credit allocation. American Political Science Review, 106(4), 703–719.

    Article  Google Scholar 

  42. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.

    Article  Google Scholar 

  43. Grumbach, J. M. (2018). From backwaters to major policymakers: Policy polarization in the states, 1970–2014. Perspectives on Politics, 16(2), 416–435.

    Article  Google Scholar 

  44. Gurin, P., Hatchett, S., Jackson, J.S. (1990). Hope and independence: Blacks’ response to electoral and party politics. Russell Sage Foundation.

  45. Harris-Lacewell, M. V. (2010). Barbershops, bibles, and BET: Everyday talk and black political thought. Princeton: Princeton University Press.

    Google Scholar 

  46. Harris, Z. S. (1954). Distributional structure. Word, 10(2–3), 146–162.

    Article  Google Scholar 

  47. Hinton, E. (2015). “A war within our own boundaries”: Lyndon Johnson’s great society and the rise of the carceral state. The Journal of American History, 102(1), 100–112.

    Article  Google Scholar 

  48. Hinton, E. (2016). From the war on poverty to the war on crime. Harvard: Harvard University Press.

    Book  Google Scholar 

  49. Hirschman, C., & Wong, M. G. (1981). Trends in socioeconomic achievement among immigrant and native-born Asian–Americans, 1960–1976. The Sociological Quarterly, 22(4), 495–514.

    Article  Google Scholar 

  50. Ho, F., & Mullen, B. V. (2008). Afro Asia: Revolutionary political and cultural connections between African Americans and Asian Americans. Duke: Duke University Press.

    Book  Google Scholar 

  51. Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945–960.

    Article  Google Scholar 

  52. Hopkins, D. J., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229–247.

    Article  Google Scholar 

  53. Hwang, W.-C., & Goto, S. (2008). The impact of perceived racial discrimination on the mental health of Asian American and Latino College Students. Cultural Diversity and Ethnic Minority Psychology, 14(4), 326.

    Article  Google Scholar 

  54. Ishizuka, K. (2016). Serve the people: Making Asian America in the long sixties. Brooklyn: Verso Books.

    Google Scholar 

  55. Joseph, P. E. (2006). The black power movement: Rethinking the civil rights-black power Era. : Taylor & Francis.

  56. Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759.

  57. Kannegaard, J.S. (2008). The press of a people: The evolution of Spanish-language news and the changing political community. PhD diss., MassachusettsInstitute of Technology.

  58. Kaufmann, K. M. (2003). Cracks in the rainbow: Group commonality as a basis for Latino and African–American political coalitions. Political Research Quarterly, 56(2), 199–210.

    Article  Google Scholar 

  59. Kim, J. (2020). Racism is not enough: Minority coalition building in San Francisco, Seattle, and Vancouver. Studies in American Political Development, 34(2), 195–215.

    Article  Google Scholar 

  60. King, D. S., & Smith, R. M. (2005). Racial orders in American political development. American Political Science Review, 99(1), 75–92.

    Article  Google Scholar 

  61. Kuramoto, F. H. (1976). Lessons learned in the federal funding game. Social Casework, 57(3), 208–218.

    Article  Google Scholar 

  62. Kwong, P. (1996). The new Chinatown. New York: Macmillan.

    Google Scholar 

  63. Lai, D.C. (2003). From downtown Slums to Suburban Malls: Chinese migration and settlement in Canada. In The Chinese Diaspora: Space, Place, Mobility, and Identity, edited by Laurence JC Ma and Carolyn L Cartier, 311–36. Rowman & Littlefield Publishers, Inc Lanham, Boulder, New York, Oxford.

  64. Lee, E. (2003). At America’s gates: Chinese immigration during the exclusion Era, 1882–1943. Carolina: University of North Carolina Press.

    Google Scholar 

  65. Li, W. (2006). From urban enclave to ethnic Suburb: New Asian communities in Pacific Rim Countries. Hawaii: University of Hawaii Press.

    Google Scholar 

  66. Lien, P., Margaret Conway, M., & Wong, J. (2004). The politics of Asian Americans: Diversity and community. Abingdon: Routledge.

    Book  Google Scholar 

  67. Linder, F., Desmarais, B., Burgess, M., & Giraudy, E. (2018). Text as policy: Measuring policy similarity through bill text reuse. Policy Studies Journal.

  68. Ling, H., & Austin, A. W. (2015). Asian American history and culture: An encyclopedia. Abingdon: Routledge.

    Book  Google Scholar 

  69. Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human Communication Research, 28(4), 587–604.

    Article  Google Scholar 

  70. Maeda, D. (2005). Black Panthers, red guards, and Chinamen: Constructing Asian American identity through performing blackness, 1969–1972. American Quarterly, 57(4), 1079–1103.

    Article  Google Scholar 

  71. Maeda, D. (2012). Rethinking the Asian American Movement. Abingdon: Routledge.

    Book  Google Scholar 

  72. Maron, M. E. (1961). Automatic indexing: An experimental inquiry. Journal of the ACM, 8(3), 404–417.

    Article  Google Scholar 

  73. Mason, L., Baxter, J., Bartlett, P.L., & Frean, M.R. (2000.) Boosting algorithms as gradient descent.” In Advances in Neural Information Processing Systems, 512–518.

  74. McClain, P. (2018). Can we all get along?: Racial and ethnic minorities in American politics. Abingdon: Routledge.

    Google Scholar 

  75. McDaniel, E. L. (2009). Politics in the pews: The political mobilization of black churches. Ann Arbor: University of Michigan Press.

    Google Scholar 

  76. McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276–282.

    Article  Google Scholar 

  77. Meng, X.-L. (2018). Statistical Paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. The Annals of Applied Statistics, 12(2), 685–726.

    Article  Google Scholar 

  78. Mikhaylov, S., Laver, M., & Benoit, K. R. (2012). Coder reliability and misclassification in the human coding of party manifestos. Political Analysis, 20(1), 78–91.

    Article  Google Scholar 

  79. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781.

  80. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I.D., Gebru, T. (2019). Model cards for model reporting.” In Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229.

  81. Mora, G. C. (2014). Making Hispanics: How activists, bureaucrats, and media constructed a new American. Chicago: University of Chicago Press.

    Book  Google Scholar 

  82. Muñoz, C. (1989). Youth, identity, power: The Chicano movement. Brooklyn: Verso.

    Google Scholar 

  83. Murakawa, N. (2014). The first civil right: How liberals built prison America. Oxford: Oxford University Press.

    Google Scholar 

  84. Nelson, L.K. (2017). Computational grounded theory: A methodological framework. Sociological Methods & Research: 0049124117729703.

  85. Nelson, L. K. (2019). To measure meaning in big data, don’t give me a map, give me transparency and reproducibility. Sociological Methodology, 49(1), 139–143.

    Article  Google Scholar 

  86. Nelson, L.K., Burk, D., Knudsen, M., McCall, L. (2017). The future of coding: A comparison of hand-coding and three types of computer-assisted text analysis methods.Sociological Methods & Research: 0049124118769114.

  87. Ngai, M. M. (2014). Impossible subjects: Illegal aliens and the making of modern America. Princeton: Princeton University Press.

    Book  Google Scholar 

  88. Omi, M., & Winant, H. (1986). Racial formation in the United States: from the1960s to the 1990s (2nd ed.). New York: Routledge.

    Google Scholar 

  89. Omi, M., & Winant, H. (1994). Racial formation in the United States: from the 1960s to the 1990s (2nd ed.). New York: Routledge.

    Google Scholar 

  90. Orleck, A. (2011). The war on poverty from the grass roots up. In A. Orleck & L. G. Hazirjian (Eds.), The war on poverty: A new grassroots history, 1964–1980. Athens: University of Georgia Press.

    Google Scholar 

  91. Pierson, P. (2003). Big, slow-moving, and . . . invisible: Macrosocial processes in the study of comparative politics. Edited by James Mahoney and Dietrich Rueschemeyer: 177–207.

  92. Prashad, V. (2002). Everybody was Kung Fu Fighting: Afro-Asian connections and the Myth of Cultural Purity. Beacon: Beacon Press.

    Google Scholar 

  93. Reardon, S. F., Kalogrides, D., & Shores, K. (2019). The geography of racial/ethnic test score gaps. American Journal of Sociology, 124(4), 1164–1221.

    Article  Google Scholar 

  94. Roberts, M.E., Stewart, B.M., & Tingley, D. (2015). STM: R package for structural topic models. R Package Version 1.1. 0.

  95. Rodriguez, A. (1999). Making Latino news: Race, language, class. Thousand Oaks: SAGE Publications.

    Book  Google Scholar 

  96. Rothstein, R. (2017). The color of law: A forgotten history of how our government segregated America. New York: Liveright Publishing.

    Google Scholar 

  97. Self, R. O. (2005). American babylon: Race and the struggle for postwar Oakland. Princeton: Princeton University Press.

    Google Scholar 

  98. Sides, J. (2006). LA city limits: African American Los Angeles from the great depression to the present. California: University of California Press.

    Google Scholar 

  99. Skocpol, T., & Theda, S. (1979). States and social revolutions: A comparative analysis of France. Russia: Cambridge University Press.

    Book  Google Scholar 

  100. Slater, D., & Ziblatt, D. (2013). The enduring indispensability of the controlled comparison. Comparative Political Studies, 46(10), 1301–1327.

    Article  Google Scholar 

  101. Soss, J., Hacker, J. S., & Mettler, S. (2007). Remaking America: Democracy and public policy in an age of inequality. New York: Russell Sage Foundation.

    Google Scholar 

  102. Suen, H. K., & Lee, P. S. C. (1985). Effects of the use of percentage agreement on behavioral observation reliabilities: a reassessment. Journal of Psychopathology and Behavioral Assessment, 7(3), 221–234.

    Article  Google Scholar 

  103. Tate, K. (1993). From protest to politics: The new black voters in American elections. Harvard: Harvard University Press.

    Google Scholar 

  104. Tibshirani, R. (1996). Regression Shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, 58(1), 267–288.

    Google Scholar 

  105. Trounstine, J. (2018). Segregation by design: Local politics and inequality in American Cities. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  106. Umemoto, K. (1989). “On Strike!” San Francisco state college strike, 1968–69: The role of Asian American students. Amerasia Journal, 15(1), 3–41.

    Article  Google Scholar 

  107. Vincent, T. G. (1973). Voices of a Black Nation: Political journalism in the Harlem Renaissance. : Ramparts Press.

  108. Watkins, R. (2012). Black power, yellow power, and the making of revolutionary identities. Jackson: University Press of Mississippi.

    Book  Google Scholar 

  109. Wei, W. (1993). The Asian American movement. Philadelphia: Temple University Press.

    Google Scholar 

  110. Wilkerson, J., & Casas, A. (2017). Large-scale computerized text analysis in political science: opportunities and challenges. Annual Review of Political Science, 20, 529–544.

    Article  Google Scholar 

  111. Williams, D. R., Lawrence, J. A., & Davis, B. A. (2019). Racism and health: Evidence and needed research. Annual Review of Public Health, 40, 105–125.

    Article  Google Scholar 

  112. Wolman, H. (1986). The Reagan urban policy and its impacts. Urban Affairs Quarterly, 21(3), 311–335.

    Article  Google Scholar 

  113. Wong, J. S., Karthick Ramakrishnan, S., Lee, T., Junn, J., & Wong, J. (2011). Asian American political participation: emerging constituents and their political identities. New York: Russell Sage Foundation.

    Google Scholar 

  114. Yu, B. (2013). Stability. Bernoulli, 19(4), 1484–1500.

    Article  Google Scholar 

  115. Zaller, J. R., et al. (1992). The nature and origins of mass opinion. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  116. Zhang, H. (2005). Exploring conditions for the optimality of Naive Bayes. International Journal of Pattern Recognition and Artificial Intelligence, 19(02), 183–198.

    Article  Google Scholar 

  117. Zhou, M. (2010). Chinatown: The socioeconomic potential of an urban enclave. Philadelphia: Temple University Press.

    Google Scholar 

  118. Zipf, G. K. (1936). The psycho-biology of language: An introduction to dynamic philology. Abingdon: Routledge.

    Google Scholar 

  119. Zipf, G. K. (1949). Human behavior and the principle of least effort. Boston: Addison-Wesley.

    Google Scholar 

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  1. Department of Political Science, University of California, Berkeley, 210 Barrows Hall 1950, Berkeley, CA, 94720, USA

    Jae Yeon Kim

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Kim, J.Y. Integrating human and machine coding to measure political issues in ethnic newspaper articles. J Comput Soc Sc 4, 585–612 (2021). https://doi.org/10.1007/s42001-020-00097-2

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