The Maya Effect and Academia

How race and gender shape who gets to collaborate, who gets listened to, and who gets credit in universities.

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Research collaboration is an institutionalized expectation and norm in academia, especially in STEM fields. But in academic work, not everyone gets equal credit when working in teams.

This phenomena has long been understood through two ideas: the Matthew Effect, where famous or high-status people get more credit than they deserve, and the Matilda Effect, where women often get less credit and are overlooked in collaborations.

But after studying the experiences of co-workers who share departments and rank but differ by race and gender, researchers have coined the Maya Effect to show how both race and gender influence how credit is shared in a public research university in the United States.

“In our study women of color often faced unique challenges in collaborations, such as having their contributions ignored, being excluded from important decisions, or having to constantly prove their value,” write the researchers in the journal Gender & Society.

For example, Yang, a foreign-born Asian woman described the silencing she experiences at work:

They invite you to be a collaborator, but they treat you like [a] student. It’s like, they don’t even listen to you. . . . And every time you bring your opinion, they’re like, ‘No, no, this is not a focus on you.’ Two months later, [the team goes] back to the argument that you made.

The study also found that the lack of respect can have material consequences. For example, foreign-born Leyla whose postdoc advisor gave her draft paper to another student, who then published the argument. When challenged, her advisor claimed that her male peer ‘independently worked on this paper.’

“Leyla learned the hard way that some scientists may steal your ideas; yet given her lower rank, nationality, and minority status, she has little recourse,” write the researchers.

When exploring tenure and promotion, the study revealed how gender biases give men more credit. For example, Courtney, who is white, told researchers:

Women who are in a collaboration are there because we’re weak, and we need collaborators; men who are in collaboration are there because they’re brilliant and needed. [laughs] How do you fight that? You know, I don’t know how to fight that.

And this from Molly, also white:

It’s true that men will get more credit. Women need lots more independent money and papers. If you’re female, [collaborations] would definitely count less.

In concluding, the researchers call for changes to organizational routines, and argue that truly understanding inequality in science and academia will require a move beyond the Matthew and Matilda effects.

“While the most pressing concern might be how women of color are less likely to be credited for their work, it is important to understand how, at every juncture in their collaborations, they face disparagement and disadvantage relative to colleagues in their same department and career stage,” they write.

The full article can be downloaded here or requested through your school or library using this citation: Mickey, E., Misra, J., Smith-Doerr, L., & Kane-Lee, E. S. (2026). The Maya Effect: Theorizing Beyond Matthew and Matilda Effects to an Intersectional Understanding of Collaboration. Gender & Society, 40(3), 386-415.

D.L. Lee is the author of SISTERLY LOVE, a novel about two sisters who grow apart.

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