Alessandro N. Vargas
I
The learning process varies according to the individual. Some individuals may experience a quick learning curve, while others may find it slower. Learning is important, no doubt. Without learning, it becomes almost impossible to thrive in this fast-paced world.
When the task in mind is learning the contents of an academic paper, the student faces a different challenge. When attempting to learn the contents of an academic paper, students often encounter unique challenges. These challenges arise because academic papers are often full of specialized jargon and technical terminology, which can be difficult to interpret. My approach to grasping the contents of academic papers and their main ideas is as follows.
The first point that I consider when a paper comes to my hands is to ask: Is this paper worth reading? What is the main contribution of this paper? With a flood of articles published each year, reaching 7 million in 2014 [1], navigating through the vast literature poses challenges.
The second point that I consider is whether the paper is authored by researchers known for producing high-quality content and whether the topic is one I am passionate about. If I get 'yes' to these questions, I read the paper in detail. I start by reviewing the results section, simulations, and graphics to get an overview of the paper's main contribution, even before reading it in full. For me, the quality of the figures is a key factor in evaluating a paper. Clear labeling, appropriate scale, and accurate data representation are some criteria I look for in high-quality figures. I prefer not to spend time on papers with poorly designed figures, as they may imply poor science. In today's distracting world, why should we spend time on a paper that brings poor science?
After checking the results and grasping the paper's meaning through the figures, I move on to evaluating the theoretical findings. If the findings are well-supported by literature, logical and coherent, and impactful, I record the paper details to my posterior analysis database. Eventually, this paper will be useful in some research I will develop. This allows me to refer back to it in the future and potentially use it to inspire new research ideas.
[1] "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