Title: Heuristics for pre-selecting scientific works (tentative)
Mentor: Francesco Lelli
This thesis is about trying to answering the following questions: “what is a good research paper?” or “is this publication worth reading?” before reading the paper.
Nowadays the amount of scientific journals is overwhelming and constantly increasing. Students, as well as senior scientists, are facing an impossible amount of information to elaborate. Consequently, they are force to apply a set of heuristics for selecting the information to process and consume. This approach force the scholar in forming an opinion prior a proper read of the scientific article. This is “by definition” a bias that may be misleading.
You will investigate how scholars implement in an explicit or implicit manner these heuristics. You will also devise best practices and computer driven tools that could help improve the efficiency of the selection process of scientific articles.
If you are curious and you what to know more about the topic, I recommend you the following:
- Read this article about my personal heuristics for understanding if a paper is worth reading.
- A generic article in Wikipedia about Heuristics: https://en.wikipedia.org/wiki/Heuristics_in_judgment_and_decision-making
- A few keywords that you may want to use in google scholar: Heuristics and Bias, Heuristics in decision making, Heuristics in information management, Heuristics in software engineering.
This project does not involve an internship. Instead, it will try to have a high academic relevance and theoretical contribution and, based on the quality of your work the candidate may be able to publish the results in the proceedings and scientific journals.
You may propose a qualitative or quantitative approach for validating the claims that you will be making in your thesis.
The ideal candidate is a self started go-getter student that like to see things thru and do not limit himself to take facts for granted.
If this topic trigger your intellectual curiosity, just get in touch with me
