Cited by 55Education, Achievement, and GiftednessSchool Choice and PerformanceDevelopmental and Educational Neuropsychology

Stigma and stratification limiting the math course progression of adolescents labeled with a learning disability

Open access
Shifrer, Dara·2016·Learning and Instruction

Integrity signals

Registry

Crossref · 10.1016/j.learninstruc.2015.12.001

Cites

76 of 76 references verified0 citation errors · 0 not checkable

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  • Teacher expectations have been found to be systematically biased toward different groups of students based on socioeconomic background, country of origin, gender, or disability status.

    “Furthermore, since teacher expectations have been found to be systematically biased toward different groups of students (based on student socioeconomic background, country of origin, gender, or disability status; e.g., Lorenz et al., 2016; Shifrer, 2016), the observed expectancy effects might contribute to educational inequalities.”

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Authors
Shifrer, D.
Affiliations
Portland State University (US)
Corresponding author
Shifrer, Dara
Venue
Learning and Instruction
Publisher
Elsevier BV
Published
2016-04
Volume
42
Pages
47-57
ISSN
0959-4752 (ISSN-L)
OpenAlex
W2204231571
Cited by
55
References
76
Citations by year
2026: 5 · 2025: 6 · 2024: 5 · 2023: 8 · 2022: 4 · 2021: 7 · 2020: 7 · 2019: 5 · 2018: 5 · 2017: 3
FWCI
4.02
Type
journal article
Language
en
Open access
Locations
Learning and Instruction (acceptedVersion)
Funders
National Science Foundation (HRD-0834177, HRD-0965444, HRD-1132028)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
  • Social Sciences › Education › School Choice and Performance
  • Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
Keywords
learning disabilities, stigma, educational stratification, adolescents, teacher expectations, teacher attributions, course placement, achievement gap, Education Longitudinal Study of 2002

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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.

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