Academic Jargon

Here is a shortlist of some jargon used in academia

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In this post, I would like to share some jargon about academic research. These are words and acronyms frequently used in the research community, but might be unknown to those that do not read academic research regularly.

As I am part of the management field, the list is likely biased.

Here they are:

Assistant professor: It is the first job someone can get in a research department. An assistant professor is usually a young scholar, in the years right after completing the Ph.D. An assistant professor is on the Tenure clock and in the track of getting tenure.

Tenure clock: It refers to a generic window of time after a Ph.D. student finishes the Ph.D. and can get a tenure position. It refers to the period in which the student is “hireable” as an assistant professor, then as a tenured professor (if s/he passes the probation). The window is not fixed; it can last between 5 to 7 years after the Ph.D. is completed.

Tenure Track: It refers to the position where an assistant professor is looking to get a tenured position. It refers to when a professor is in the “track” of getting a tenured position.

Tenure: It refers to the position where the professor has a more stable job, where s/he can research more freely. Though it is certainly not impossible, it is hard to fire a tenured professor. It usually takes legal or ethical problems to fire one. The idea is that job stability provides better thinking and more creativity in a professor’s research projects, leading to better and more impactful research.

Teaching portfolio: It is part of the dossier necessary for a job application of a tenure track position. It contains statements and definitions about the applicant’s teaching philosophy, teaching evaluations, syllabus style, assignments, and other teaching related materials.

Paper: It is a generic name for a research/study that is intended to be published in an academic journal. The words article and manuscript are synonyms.

Job Market Paper: Usually, Ph.D. candidates focus on one paper to be his/her job market paper. This paper is usually very well developed, with substantial implications and high contributions to the related literature. It is expected that a Ph.D. has at least one paper with substantial contributions (i.e., the job market paper) rather than many papers with less contribution. Quality defeats quantity here because a well-thought high-potential paper takes more time than several lower-quality papers.

Cover letter: It is a one- to two-page document that states the purpose and share any confidential information about a paper that is submitted to a journal.

Peer review: It refers to the evaluation process of a paper before it is published. Usually, two or three experts provide a report with suggestions to authors about the strengths and weaknesses of the submitted paper. Then, the authors attend the suggestions and resubmit the paper to the journal. This process can take as many rounds as it is necessary until the paper is fit to be published. It usually takes two to three rounds. In any round, a submitted paper can be denied publication, if the experts find a critical weakness or a mistake that hampers the paper’s contribution. The process is usually double-blinded, which means that the experts and the authors do not know the identity of each other.

Journal’s editor: An editor is an experienced researcher that deals with the peer review process by creating the bridge between the authors and reviewers. It is the editor that has the final word of acceptance or rejection of a submitted paper.

Impact factor (IF): It is a score that reflects how many citations a journal has over the last periods. It demonstrates the importance/relevance of that journal. In management and finance research, IFs of journals with a strong reputation are commonly between 2 and 5. Other journals, like Nature, can have IF over 40 or 50.

H-index: It refers to how many h articles with h citations an author has. For instance, if an author has at least five published articles with five citations each, the h-index is five. If the author publish a 6th article, the h-index only increases to six, when the six articles achieve six citations each.

Dark Data: It refers to the fact that research data is typically a closed box, and only the authors have access to it. This creates questions about the credibility of that research. Open Science practices mitigate this problem by making research data open to anyone.

Publication bias: It refers to the fact that journals usually do not publish studies that did not find significant results, because they sound less interesting and worthy than studies that find significant results. Journals also avoid non-significant studies because they receive fewer citations. The consequence is that the collective pool of published studies is massively dominated by studies with significant results. At the aggregate level, the public may never know the outcomes of those studies that did not find significant results because they are rarely published.

P-hacking: It refers to the massive manipulation of a research model to find statistically significant results. This is a bad research practice because it means the authors likely modified the model to guarantee significant results and, therefore, increase the likelihood of acceptance. If authors change the model to find significant results, the results are not trustworthy because significance was found as a consequence of the authors' modification. P-hacking is also known as Data hacking, Data snooping, or Data fishing.

HARKing: It means Hypothesizing After the Results are Known. It means that authors changed their theory to explain the results, rather than present the results to test the theory. Authors also might exclude or retroactively include research hypotheses to fit the results. HARKing is a bad research practice because the theory should always be defined before authors have their results. Also, HARKing implies lack of transparency and credibility in anyone’s research.

FAIR: Findable, Accessible, Interoperable, and Reusable. It refers to necessary characteristics of the storage (at the machine-level) of academic research data in order to facilitate the access by the public.

This list is far from being exhaustive. I shall update it in the future.

Thanks for passing by!

Henrique Castro Martins
Henrique Castro Martins
Assistant Professor of Finance
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