Home Limited diffusion of scientific knowledge forecasts collapse

Limited diffusion of scientific knowledge forecasts collapse

· December 6, 2024 · 0 Comment
  1. Arthur, W. B. Complexity in economic and financial markets. Complexity 1, 20–25 (1995).
  2. Article  Google Scholar 
  3. Harras, G. & Sornette, D. How to grow a bubble: a model of myopic adapting agents. J. Econ. Behav. Organ. 80, 137–152 (2011).
  4. Article  Google Scholar 
  5. Goldman, A. I. & Shaked, M. An economic model of scientific activity and truth acquisition. Philos. Stud. 63, 31–55 (1991).
  6. Article  Google Scholar 
  7. Pedersen, D. B. & Hendricks, V. F. Science bubbles. Philos. Technol. 27, 503–518 (2014).
  8. Article  Google Scholar 
  9. Evans, J. P., Meslin, E. M., Marteau, T. M. & Caulfield, T. Genomics. Deflating the genomic bubble. Science 331, 861–862 (2011).
  10. Article CAS PubMed  Google Scholar 
  11. Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).
  12. Partha, D. & David, P. A. Toward a new economics of science. Res. Policy 23, 487–521 (1994).
  13. Article  Google Scholar 
  14. Small, H., Boyack, K. W. & Klavans, R. Identifying emerging topics in science and technology. Res. Policy 43, 1450–1467 (2014).
  15. Article  Google Scholar 
  16. Funk, R. J. & Owen-Smith, J. A dynamic network measure of technological change. Manage. Sci. 63, 791–817 (2016).
  17. Article  Google Scholar 
  18. Klavans, R., Boyack, K. W. & Murdick, D. A. A novel approach to predicting exceptional growth in research. PLoS ONE 15, e0239177 (2020).
  19. Article CAS PubMed PubMed Central  Google Scholar 
  20. Weis, J. W. & Jacobson, J. M. Learning on knowledge graph dynamics provides an early warning of impactful research. Nat. Biotechnol. 39, 1300–1307 (2021).
  21. Article CAS PubMed  Google Scholar 
  22. Lin, Y., Evans, J. A. & Wu, L. New directions in science emerge from disconnection and discord. J. Informetr. 16, 101234 (2022).
  23. Article  Google Scholar 
  24. Petersen, A. M., Pan, R. K., Pammolli, F. & Fortunato, S. Methods to account for citation inflation in research evaluation. Res. Policy 48, 1855–1865 (2019).
  25. Article  Google Scholar 
  26. Hutchins, B. I., Yuan, X., Anderson, J. M. & Santangelo, G. M. Relative Citation Ratio (RCR): a new metric that uses citation rates to measure influence at the article level. PLoS Biol. 14, e1002541 (2016).
  27. Article PubMed PubMed Central  Google Scholar 
  28. Taylor, M. & Heath, B. Years after Brigham–Harvard scandal, U.S. pours millions into tainted stem-cell field. Reuters (21 June 2022).
  29. Anversa, P., Kajstura, J., Leri, A. & Bolli, R. Life and death of cardiac stem cells: a paradigm shift in cardiac biology. Circulation 113, 1451–1463 (2006).
  30. Article PubMed  Google Scholar 
  31. 2009 Current Fiscal Year Report: Board of Scientific Counselors, National Institute on Aging. The Federal Advisory Committee Act (FACA) Database (Department of Health and Human Services, 2009); https://www.facadatabase.gov/FACA/apex/FACACommitteeLevelReportAsPDF?id=a10t0000001h2ObAAI
  32. Murry, C. E. et al. Haematopoietic stem cells do not transdifferentiate into cardiac myocytes in myocardial infarcts. Nature 428, 664–668 (2004).
  33. Article CAS PubMed  Google Scholar 
  34. Vrotsos, L. W. Harvard Medical School requests retractions for former professor’s research. The Harvard Crimson (16 October 2018).
  35. Oransky, I. & Marcus, A. Harvard and the Brigham call for more than 30 retractions of cardiac stem cell research. STAT News (14 October 2018).
  36. Davis, D. R. Cardiac stem cells in the post-Anversa era. Eur. Heart J. 40, 1039–1041 (2019).
  37. Article PubMed  Google Scholar 
  38. Osafune, K. et al. Marked differences in differentiation propensity among human embryonic stem cell lines. Nat. Biotechnol. 26, 313–315 (2008).
  39. Article CAS PubMed  Google Scholar 
  40. Harris, R. Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions (Basic Books, 2017).
  41. Neimark, J. Line of attack. Science 347, 938–940 (2015).
  42. Article CAS PubMed  Google Scholar 
  43. Hughes, P., Marshall, D., Reid, Y., Parkes, H. & Gelber, C. The costs of using unauthenticated, over-passaged cell lines: how much more data do we need? Biotechniques 43, 575–586 (2007).
  44. Article  Google Scholar 
  45. Xu, J. et al. Building a PubMed knowledge graph. Sci. Data 7, 205 (2020).
  46. Article PubMed PubMed Central  Google Scholar 
  47. Teplitskiy, M., Acuna, D., Elamrani-Raoult, A., Körding, K. & Evans, J. The sociology of scientific validity: how professional networks shape judgement in peer review. Res. Policy 47, 1825–1841 (2018).
  48. Article  Google Scholar 
  49. Belikov, A. V., Rzhetsky, A. & Evans, J. Prediction of robust scientific facts from literature. Nat. Mach. Intell. 4, 445–454 (2022).
  50. Article  Google Scholar 
  51. Quaini, F. et al. Chimerism of the transplanted heart. N. Engl. J. Med. 346, 5–15 (2002).
  52. Article PubMed  Google Scholar 
  53. Freeman, G. J. et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J. Exp. Med. 192, 1027–1034 (2000).
  54. Article CAS PubMed PubMed Central  Google Scholar 
  55. Azoulay, P., Fons-Rosen, C. & Zivin, J. S. G. Does science advance one funeral at a time? Am. Econ. Rev. 109, 2889–2920 (2019).
  56. Article PubMed PubMed Central  Google Scholar 
  57. Le, Q. & Mikolov, T. Distributed representations of sentences and documents. In Proc. 31st International Conference on Machine Learning (eds Xing, E. P. & Jebara, T.) 1188–1196 (PMLR, 2014).
  58. Laflamme, M. A. & Murry, C. E. Regenerating the heart. Nat. Biotechnol. 23, 845–856 (2005).
  59. Article CAS PubMed  Google Scholar 
  60. van Berlo, J. H. et al. C-kit+ cells minimally contribute cardiomyocytes to the heart. Nature 509, 337–341 (2014).
  61. Article PubMed PubMed Central  Google Scholar 
  62. Chien, K. R. et al. Regenerating the field of cardiovascular cell therapy. Nat. Biotechnol. 37, 232–237 (2019).
  63. Article CAS PubMed  Google Scholar 
  64. Mellman, I., Coukos, G. & Dranoff, G. Cancer immunotherapy comes of age. Nature 480, 480–489 (2011).
  65. Article CAS PubMed PubMed Central  Google Scholar 
  66. Finck, A., Gill, S. I. & June, C. H. Cancer immunotherapy comes of age and looks for maturity. Nat. Commun. 11, 3325 (2020).
  67. Article CAS PubMed PubMed Central  Google Scholar 
  68. Smyth, M. J. & Teng, M. W. 2018 Nobel Prize in physiology or medicine. Clin. Transl. Immunol. 7, e1041 (2018).
  69. Article  Google Scholar 
  70. Lin, J. & Wilbur, W. J. PubMed related articles: a probabilistic topic-based model for content similarity. BMC Bioinformatics 8, 423 (2007).
  71. Article PubMed PubMed Central  Google Scholar 
  72. Azoulay, P., Bonatti, A. & Krieger, J. L. The career effects of scandal: evidence from scientific retractions. Res. Policy 46, 1552–1569 (2017).
  73. Article  Google Scholar 
  74. Myers, K. The elasticity of science. Am. Econ. J. Appl. Econ. 12, 103–134 (2020).
  75. Article  Google Scholar 
  76. Reschke, B. P., Azoulay, P. & Stuart, T. E. Status spillovers: the effect of status-conferring prizes on the allocation of attention. Adm. Sci. Q. 63, 819–847 (2018).
  77. Article  Google Scholar 
  78. Danchev, V., Rzhetsky, A. & Evans, J. A. Centralized scientific communities are less likely to generate replicable results. eLife 8, e43094 (2019).
  79. Article PubMed PubMed Central  Google Scholar 
  80. Bourdieu, P. The specificity of the scientific field and the social conditions of the progress of reason. Soc. Sci. Inf. 14, 19–47 (1975).
  81. Article  Google Scholar 
  82. Kim, J., Wang, Z., Shi, H., Ling, H.-K. & Evans, J. Individual misinformation tagging reinforces echo chambers; collective tagging does not. Preprint at https://arxiv.org/abs/2311.11282 (2023).
  83. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013).
  84. Google Scholar 
  85. Kozlowski, A. C., Taddy, M. & Evans, J. A. The geometry of culture: analyzing the meanings of class through word embeddings. Am. Sociol. Rev. 84, 905–949 (2019).
  86. Article  Google Scholar 
  87. Garg, N., Schiebinger, L., Jurafsky, D. & Zou, J. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl Acad. Sci. USA 115, E3635–E3644 (2018).
  88. Article CAS PubMed PubMed Central  Google Scholar 
  89. Perozzi, B., Al-Rfou, R. & Skiena, S. DeepWalk: online learning of social representations. In Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 701–710 (Association for Computing Machinery, 2014).
  90. Grover, A. & Leskovec, J. node2vec: scalable feature learning for networks. KDD 2016, 855–864 (2016).
  91. PubMed PubMed Central  Google Scholar 
  92. Rehurek, R. & Sojka, P. Software framework for topic modelling with large corpora. In Proc. LREC 2010 Workshop on New Challenges for NLP Frameworks 45–50 (Univ. of Malta, 2010).
  93. Foster, J. G., Rzhetsky, A. & Evans, J. A. Tradition and innovation in scientists’ research strategies. Am. Sociol. Rev. 80, 875–908 (2015).
  94. Article  Google Scholar 
  95. Azoulay, P., Furman, J. L. & Murray, F. Retractions. Rev. Econ. Stat. 97, 1118–1136 (2015).
  96. Article  Google Scholar 
  97. de Solla Price, D. J. Little Science, Big Science—and Beyond (Columbia Univ. Press, 1963).
  98. Kang, D. Limited diffusion of scientific knowledge forecasts collapse. GitHub https://github.com/Donghyun-Kang-Soc/limited_diffusion (2024).

toan_sg_2023

Leave a Reply

Your email address will not be published. Required fields are marked *