Alessandro N. Vargas
The learning process varies according to the individual. For some, the learning curve is quick, and for others, the learning curve is slow. For both groups, learning is important. Without learning, it becomes almost impossible to thrive in his or her objectives.
When the task in mind is learning the contents of an academic paper, the student faces a different challenge. The challenge relies on the fact that the academic paper contains many jargons and technical results, and most of them are difficult to interpret. My approach to grasp the academic paper's main ideas that come to my hands is as follows.
The first point that I observe is this: Why should I read this paper? Is this paper worthy of reading? Notice that we see a flood of articles published each year, soaring at 7 million in 2014 . How can we navigate among those many papers?
The second point that I observe is as follows. Is this paper written by a group of researchers that usually produce high-quality content? Is this paper is a topic that I am deeply passionate about? If the answer is "yes" to all these questions, I read the paper in full detail. I first check the result section, simulation, graphics, and I try to grasp the main paper's contribution without reading it. The quality of the figures is the key factor for me. I do not continue to read the paper if the figures are poorly designed. Unfortunately, poor figures seem to imply poor science. And in today's distracting world, who would like to spend time on a paper that brings poor science?
After I have checked the results and have grasped the paper's meaning through the figures, I move to the theoretical findings. And finally, if the theoretical findings sound good, I record the paper details in my posterior analysis database. Eventually, this paper will be useful in some research that I will develop. For instance, this paper can bring me ideas for extensions or up my mind to new research lines.
 "Over-optimization of academic publishing metrics: observing Goodhart's Law in action" by Michael Fire and Carlos Guestrin. https://doi.org/10.1093/gigascience/giz053