• Key features of the R-Factor
  • Critique and rebuttal: 1. Difference between Review and R-factors, 2. Publication bias and R-Factor 3. claim vs. paper. What can the R-factor assess, what remains impssible. 4. The weight question: Should all follow-up studies considered equal? 5.
  • Introducing the linker: R-Factor can be easily displayed and claims/ideas and how they evolved in the literature can be tracked.
  • Reiterating how R-Factor is relevant to all fields, could disrupt the wrong prevailing incentive system. (Vision)
  • What comes next? Problem: We can't score all citations of all papers: An algorithm would be the way forward! (some details on what we have been doing so far in terms of algorithm ) Help needed. Or funding. To get funding we created a company and applied for grants. However, to go make this a reality we need the input from those who are affected most: All sorts of researchers of all sorts of fields and all sorts of career stages. We plan to further grow the corpus. The annotated corpus itself will be distributed under Creative Commons license and available for everyone! For the algorithm: We are considering an Apache 2.0 license.
  • Call to action: Score! Link to the shoouter and GIF explaining.