The Howard‐Harvard effect: Institutional reproduction of intersectional inequalities is a research paper published in Journal of the Association for Information Science and Technology (2024). On theSindex it has a DataRank of 0.330. It has been cited 8 times.
AbstractThe production of research and faculty in the US higher education system is concentrated within a few institutions. Concentration of research and resources affects minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship between research topics and identities with institutional prestige and scientific impact. Our results show statistically significant differences between minoritized scholars and White men in citations and journal impact. The aggregate research profile of prestigious US universities is highly correlated with the research profile of White men, and negatively correlated with the research profile of minoritized women. Furthermore, authors affiliated with more prestigious institutions are associated with increasing inequalities in both citations and journal impact. These results suggest a relationship—which we coin as the Howard‐Harvard effect—in which the topical profile of minoritized scholars is further marginalized in prestigious institutions as compared to mission‐driven institutions. Academic institutions and funders should create policies to mitigate the systemic barriers that prevent the United States from achieving a fully robust scientific ecosystem.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.330
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.