Evidence map›Paper›PMID 40620687›Full record

ArticleResearch in higher education2025

Letters of Recommendation by High School Counselors in Selective College Admissions: Differences by Race and Socioeconomic Status in Letter Length and Topics Discussed.

Brian Heseung Kim, Julie J Park, Pearl Lo, Dominique Baker, Nancy Wong, Stephanie Breen, Huong Truong, Jia Zheng, Kelly Rosinger, OiYan A Poon

Abstract read
In one paragraph

Article in Research in higher education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Brian Heseung KimCommon App, Arlington, USA.
Julie J ParkUniversity of Maryland, College Park, USA.ORCID 0000-0002-8431-6381
Pearl LoUniversity of Maryland, College Park, USA.
Dominique BakerUniversity of Delaware, Newark, DE USA.
Nancy WongUniversity of Maryland, College Park, USA.
Stephanie BreenUniversity of Maryland, College Park, USA.
Huong TruongUniversity of Maryland, College Park, USA.
Jia ZhengUniversity of Massachusetts, Amherst, USA.
Kelly RosingerPennsylvania State University, University Park, PA USA.
OiYan A PoonUniversity of Maryland, College Park, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Letters of recommendation from school counselors are required to apply to most selective universities. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted through the Common Application. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to race/ethnicity, sex, and proxies for socioeconomic status. We explore whether differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics; letters written by the same counselor; and for students with higher standardized test scores. We found noteworthy naïve differences in letter length and content across nearly all demographic groups, many reflecting known inequities (e.g., longer letters and more sentences on Personal Qualities for private school students). However, the ultimate implications of these patterns hinge on exactly Supplementary Information: The online version of this article contains supplementary material available 10.1007/s11162-025-09847-5.

Indexed as

AdmissionsCounselorsDiversityEquityLetters of recommendation

Identifiers

PMID40620687
PMCPMC12227477

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.