Equal time for equal crime? Racial bias in school discipline
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Black students are more likely to receive suspensions than White students even when they have misbehaved a similar number of times, are engaged in the same incident of misbehavior, have similar prior behavioral tendencies, and are in schools with similar racial compositions.
“Black students are more likely to receive suspensions than White students, even when the students have misbehaved a similar number of times”
From Fostering Belonging, Transforming Schools: The Impact of Restorative Practices (2023) · cites this paper as Shi & Zhu, 2022
The citation lists haven’t been fetched yet
References 27 · Cited by 25
- Authors
- Shi, Y., & Zhu, M.
- Affiliations
- Syracuse University (US)
- Corresponding author
- Zhu, Maria
- ORCID
- Venue
- Economics of Education Review
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 88
- Pages
- 102256
- ISSN
- 0272-7757 (ISSN-L)
- OpenAlex
- W3155270740
- Cited by
- 25
- References
- 27
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 9
- FWCI
- 11.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Economics of Education Review (publishedVersion)
- Topics
- Social Sciences › Education › Education Discipline and Inequality
- Social Sciences › Education › School Choice and Performance
- Social Sciences › Sociology and Political Science › Critical Race Theory in Education
- Keywords
- student discipline, racial bias, suspension, racial disparities, Black students, Hispanic students, white students, student behavior, exclusionary discipline, individual fixed effects
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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