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Understanding Citation Imbalances and Gendered Citation Practices

Description

Science has been experiencing vast gender imbalances in academic participation. Such inequalities have also been found in compensation, grant funding, hiring and promotions, authorship, and citations. Despite recent progress in these areas, the presence of disparities in scholarship engagement may result in long-term inequities in other areas. This imbalance can be attributed to the 'Matilda effect' in which men's contributions are seen as more central and valued, whereas women's contributions are under-appreciated and under-discussed. The study of citation dynamics is an important endeavor for understanding and addressing biases in science because of the potential downstream effects of inequitable engagement with women-led and men-led work.

In this project, we are interested to study how citation imbalances might be amplified or reduced due to online visibility. We plan to leverage Altmetric dataset to measure online visibility. Our long-term goal is to understand the role social media (Twitter) plays in citation dynamics.

The ideal student candidate will:

  • be proficient in Python/R
  • have an interest in data science
  • be eager to learn new skills

You will gain experience with methods in data science and meta-science, advance our understanding of the science-of-science, and have the opportunity to contribute to academic publications and open-source software.

MIT Libraries is an environment that welcomes anybody, any mind – including all genders – and particularly encourages applications from underrepresented minorities, women, disabled applicants, and veterans.

Compensation

$20/hour

Application information

To apply, send a resume to Suman Maity, Postdoctoral Researcher, CREOS.

Work hours

8-10 hours per week (flexible)

Weekly meeting with the Researcher

Student participation dates

Spring 2023

Summer 2023