Warning: package 'openalexR' was built under R version 4.5.2Part 2: Conducting a literature scan
Intro to Computational Studies in Education and the Social Sciences
Using Posit to identify an initial set of literature
Most of us use Google Scholar to find research liteature. However, there are a host of computational tools that we can use to conduct a literature scan or go more in-depth to conduct a systematic literature review.
Part of this process is related to the important process of using the literature to help us build theory.
Theory construction
To that end, good research requires knowledge of the peer-reviewed literature.
Scoping
The data for the study comes from the Web of Science (WoS) Core Collection. Our initial scoping process included a set of iterative steps to make sense of the global research literature on the various notions of racism in STEM. We prioritized three citation indexes in our searches between the period from 2015 to 2024. Our analysis focused on journal articles written in English in the Education, Special Education, and related Education Scientific Disciplines.
- Science Citation Index Expanded, SCI-EXPANDED (2002-present)
- Social Sciences Citation Index SSCI (2002-present)
- Arts and Humanities Citation Index ACHI (2002-present)
- Emerging Sources Citation Index ESSI (2012-present)
Timespan: 2014-01-01 to 2024-12-31
Document Types: Article
Inclusion and Exclusion Criteria
| Code | Criteria |
|---|---|
| IC1 | Article contains STEM and one of the notions in the title (TI) or abstract (AB): racism, “white supremacy,” colonialism, xenophobia, nationalism, antiasian, anti-Asian[*], antiblack, Anti-Black[*] |
| IC2 | Article published between 2014 and 2024 |
| IC3 | Article originally written in English |
| IC4 | Article is a journal article |
| IC5 | Article purpose or core questions center on the topical subjects of analysis |
Review
Key Columns of Interest:
AU: Authors of the publication
AB: Abstract text
TI: Title of the publication
AU_CO: Countries of the authors
SC: Subject categories (e.g., “Education & Educational Research”)
PY: Publication year
TC: Total citations
Descriptive (performance) analysis
Summary of the data set and documents.
Publication-related metrics
Main information
Main information about the collection.
$MainInformationDF
Description Results
1 MAIN INFORMATION ABOUT DATA
2 Timespan 2014:2025
3 Sources (Journals, Books, etc) 256
4 Documents 320
5 Annual Growth Rate % - 18.11
6 Document Average Age 4.95
7 Average citations per doc 12.56
8 Average citations per year per doc 1.817
9 References 18413
10 DOCUMENT TYPES
11 article 295
12 article; early access 19
13 article; proceedings paper 1
14 editorial material; early access 1
15 review; early access 4
16 DOCUMENT CONTENTS
17 Keywords Plus (ID) 703
18 Author's Keywords (DE) 1053
19 AUTHORS
20 Authors 889
21 Author Appearances 912
22 Authors of single-authored docs 134
23 AUTHORS COLLABORATION
24 Single-authored docs 138
25 Documents per Author 0.36
26 Co-Authors per Doc 2.85
27 International co-authorships % 9.375
28 Publications by year
$AnnualProduction
Year Articles
1 2014 9
2 2015 11
3 2016 14
4 2017 13
5 2018 14
6 2019 22
7 2020 21
8 2021 41
9 2022 47
10 2023 57
11 2024 70
12 2025 1year_counts <- M4 %>%
group_by(PY) %>%
summarise(count = n())
# Your existing plot code
pubs_by_year <- ggplot(year_counts, aes(x = PY, y = count)) +
geom_col(fill = "steelblue") +
geom_text(aes(label = count),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 3) +
geom_smooth(method = "loess", se = FALSE, color = "blue", size = 0.5, linetype = "dotted") +
theme_minimal() +
labs(x = "Year", y = "Number of Publications",
title = "") +
scale_x_continuous(breaks = seq(min(year_counts$PY), max(year_counts$PY), by = 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pubs_by_year
Most productive authors
$MostProdAuthors
Authors Articles Authors Articles Fractionalized
1 MCGEE EO 7 MCGEE EO 4.70
2 DANCY M 3 SPENCER BM 2.00
3 ADAMES HY 2 RUSSO-TAIT T 1.33
4 BROCKMAN AJ 2 BROCKMAN AJ 1.17
5 BROOKS E 2 BROOKS E 1.14
6 KIM M 2 ARAMAYO RR 1.00
7 LEYVA LA 2 ARBUCIAS D 1.00
8 MCNEILL RT 2 ARMENGOL JM 1.00
9 MICKELSON R 2 ARONS W 1.00
10 MISRA DP 2 BABAII E 1.00Most cited papers
$MostCitedPapers
Paper DOI TC TCperYear NTC
1 MCGEE EO, 2016, AM EDUC RES J 10.3102/0002831216676572 230 20.91 4.99
2 MCGEE EO, 2020, EDUC RESEARCHER 10.3102/0013189X20972718 222 31.71 8.05
3 CHAVEZ-DUEÑAS NY, 2019, AM PSYCHOL 10.1037/amp0000289 187 23.38 8.18
4 FUENFSCHILLING L, 2018, RES POLICY 10.1016/j.respol.2018.02.003 155 17.22 4.76
5 MCCOY DL, 2015, J DIVERS HIGH EDUC 10.1037/a0038676 88 7.33 5.63
6 KIIK L, 2016, EURASIAN GEOGR ECON 10.1080/15387216.2016.1198265 78 7.09 1.69
7 SLAUGHTER-ACEY JC, 2016, ANN EPIDEMIOL 10.1016/j.annepidem.2015.10.005 76 6.91 1.65
8 MCGEE EO, 2019, TEACH COLL REC NA 68 8.50 2.97
9 LEE MGJ, 2020, INT J STEM EDUC 10.1186/s40594-020-00241-4 66 9.43 2.39
10 HARTMAN TK, 2021, SOC PSYCHOL PERS SCI 10.1177/1948550620978023 63 10.50 4.55Main sources
$MostRelSources
Sources Articles
1 JOURNAL OF CHEMICAL EDUCATION 6
2 RACE ETHNICITY AND EDUCATION 6
3 CULTURAL STUDIES OF SCIENCE EDUCATION 5
4 JOURNAL OF RESEARCH IN SCIENCE TEACHING 5
5 ETHNIC AND RACIAL STUDIES 4
6 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 4
7 INTERNATIONAL JOURNAL OF STEM EDUCATION 4
8 JOURNAL OF DIVERSITY IN HIGHER EDUCATION 4
9 JOURNAL OF RACIAL AND ETHNIC HEALTH DISPARITIES 4
10 NATIONS AND NATIONALISM 4Citation-related metrics
Most frequently cited documents
The top 21 most cited papers. A total of 21 papers was chosen based on ties with the top 15 (8 citations).
# M4$CR[1] # identify separators
# Most frequently cited documents in the collection
CR <- citations(M4, field = "article", sep = ";")
cbind(CR$Cited[1:21]) [,1]
BRAUN V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238 17
CARLONE HB, 2007, J RES SCI TEACH, V44, P1187, DOI 10.1002/TEA.20237 14
MCGEE EO, 2016, AM EDUC RES J, V53, P1626, DOI 10.3102/0002831216676572 14
SUE DW, 2007, AM PSYCHOL, V62, P271, DOI 10.1037/0003-066X.62.4.271 13
CRENSHAW K, 1993, STANFORD LAW REVIEW VOL 43, NO 6, JULY 1991, P1241 12
MCGEE EO, 2011, AM EDUC RES J, V48, P1347, DOI 10.3102/0002831211423972 12
ONG M, 2011, HARVARD EDUC REV, V81, P172, DOI 10.17763/HAER.81.2.T022245N7X4752V2 12
SOLÓRZANO D, 2000, J NEGRO EDUC, V69, P60 12
BONILLA-SILVA E., 2021, RACISM RACISTS COLOR 11
MCGEE EO, 2017, COGNITION INSTRUCT, V35, P265, DOI 10.1080/07370008.2017.1355211 10
MCGEE EO, 2020, EDUC RESEARCHER, V49, P633, DOI 10.3102/0013189X20972718 10
DELGADO R., 2017, CRITICAL RACE THEORY 9
MARTIN DB, 2009, TEACH COLL REC, V111, P295 9
MCGEE E.O., 2020, BLACK, BROWN, BRUISED: HOW RACIALIZED STEM EDUCATION STIFLES INNOVATION 9
BONILLASILVA E, 1997, AM SOCIOL REV, V62, P465, DOI 10.2307/2657316 8
COLLINS P. H., 2020, INTERSECTIONALITY, V2ND 8
CRENSHAW KIMBERLE., 1991, MAPPING THE MARGINS: INTERSECTIONALITY, IDENTITY POLITICS, AND VIOLENCE AGAINST WOMEN OF COLOR, DOI DOI 10.2307/1229039 8
IRELAND DT, 2018, REV RES EDUC, V42, P226, DOI 10.3102/0091732X18759072 8
KENDI I. X., 2019, BE ANTIRACIST 8
LADSONBILLINGS G, 1995, TEACH COLL REC, V97, P47 8
ONG M, 2005, SOC PROBL, V52, P593, DOI 10.1525/SP.2005.52.4.593 8Keywords and Keywords Plus
Author Keywords and Keywords-Plus
$MostRelKeywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 RACISM 34 RACE 43
2 RACE 21 EXPERIENCES 31
3 STEM 15 SCIENCE 29
4 NATIONALISM 14 WOMEN 29
5 HIGHER EDUCATION 13 EDUCATION 28
6 COLONIALISM 11 IDENTITY 23
7 COVID-19 11 HEALTH 22
8 GENDER 11 STUDENTS 22
9 INTERSECTIONALITY 10 DISCRIMINATION 18
10 DIVERSITY 9 DISPARITIES 17Keyword Occurence Network
# Classical keyword co-occurrences network
NetMatrix1 <- biblioNetwork(M4, analysis = "co-occurrences", network = "keywords", sep = ";")
# statistics for the network
netstat1 <- networkStat(NetMatrix1)
summary(netstat1, k=10)
Main statistics about the network
Size 703
Density 0.016
Transitivity 0.286
Diameter 6
Degree Centralization 0.229
Average path length 2.883
# Plot the network
set.seed(3)
net1a = networkPlot(NetMatrix1,
n = 25, # Limit to top 25 keywords
normalize = "association",
Title = "Top Keyword Co-Occurrences",
type = "circle",
size = TRUE,
remove.multiple = FALSE,
labelsize = 0.7,
cluster = "none")
net1b = networkPlot(NetMatrix1,
n = 30, # Even fewer nodes
normalize = "association",
Title = "Keyword Network",
type = "kamada",
size = TRUE,
remove.multiple = TRUE,
labelsize = 0.5,
cluster = "louvain")
net1c = networkPlot(NetMatrix1,
n = 30, # Even fewer nodes
#normalize = "association",
#weighted = T,
Title = "Keyword Co-Occurence Network",
type = "fruchterman",
size = TRUE,
remove.multiple = TRUE,
labelsize = 0.5,
cluster = "louvain")
Conceptual Structure Map
Conceptual Structure
suppressWarnings(CS1 <- conceptualStructure(M4,
method="MCA",
field="ID",
minDegree=15,
clust=5,
stemming=FALSE,
labelsize=15,
documents=20)
)
# Conceptual Structure using keywords (method="CA")
CS <- conceptualStructure(M4,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)
CS <- conceptualStructure(M4,
field="ID",
method="CA",
minDegree=4,
clust=5,
stemming=FALSE,
labelsize=10, # Set to 0 to remove labels
documents=10)
# Extract coordinates and clusters
coords <- CS[[1]] # Coordinates
clusters <- CS[[2]] # Cluster assignments
CS[4]$graph_terms
# Create a historical citation network
options(width=130)
histResults <- histNetwork(M4, min.citations = 5, sep = ";")
WOS DB:
Searching local citations (LCS) by reference items (SR) and DOIs...
Analyzing 19923 reference items...
Found 29 documents with no empty Local Citations (LCS)
Legend
Label
1 MCCOY DL, 2015, J DIVERS HIGH EDUC DOI 10.1037/A0038676
2 MCGEE EO, 2016, AM EDUC RES J DOI 10.3102/0002831216676572
3 BROWN BA, 2016, J RES SCI TEACH DOI 10.1002/TEA.21249
4 MCGEE E, 2018, AERA OPEN DOI 10.1177/2332858418816658
5 LEE MGJ, 2020, INT J STEM EDUC DOI 10.1186/S40594-020-00241-4
6 MCGEE EO, 2020, EDUC RESEARCHER DOI 10.3102/0013189X20972718
7 DANCY M, 2020, INT J STEM EDUC DOI 10.1186/S40594-020-00250-3
8 VAN DUSEN B, 2020, J RES SCI TEACH DOI 10.1002/TEA.21584
9 SPENCER BM, 2021, SOCIOL FORUM DOI 10.1111/SOCF.12724
10 NISSEN JM, 2021, PHYS REV PHYS EDUC R DOI 10.1103/PHYSREVPHYSEDUCRES.17.010116
11 ALLEN D, 2022, INT J STEM EDUC DOI 10.1186/S40594-022-00334-2
12 RUSSO-TAIT T, 2022, J RES SCI TEACH DOI 10.1002/TEA.21775
13 MCNEILL RT, 2022, J LEARN SCI DOI 10.1080/10508406.2022.2073233
14 KING GP, 2023, CBE-LIFE SCI EDUC DOI 10.1187/CBE.22-06-0104
15 WILKINS-YEL KG, 2023, J RES SCI TEACH DOI 10.1002/TEA.21798
16 MCGEE EO, 2023, ETHNIC RACIAL STUD DOI 10.1080/01419870.2022.2159474
17 FORSYTHE D, 2024, J HIGH EDUC-UK DOI 10.1080/00221546.2023.2265285
Author_Keywords
1 COLORBLIND RACISM; MENTORING; STUDENTS OF COLOR
2 STEREOTYPE MANAGEMENT; STEM STUDENTS OF COLOR; RACIAL HOSTILITY IN ACADEMIA; STEM RACIAL GAP; CULTURAL BIAS
3 AFRICAN-AMERICAN; SCIENCE IDENTITY; MATRICULATION; ACCESS TO SCIENCE
4 RACIAL STEREOTYPES; STEREOTYPE LIFT; STEREOTYPE THREAT; COLLEGE STEM OUTCOMES; RACIAL TRAUMA; BLACK; ASIAN; POLITICAL RACE
5 RACIAL MICROAGGRESSIONS; HIGHER EDUCATION; STEM; EDUCATIONAL SETTING; DIVERSITY CONCERNS
6 CULTURAL ANALYSIS; DISPARITIES; DOCTORAL; ENGINEERING EDUCATION; ENTREPRENEURSHIP; HBCUS; HIGHER EDUCATION; MENTORING; MINORITIZED; RACE; STEM; STRUCTURAL RACISM; TECHNOLOGY
7 RACE; GENDER; UNDERGRADUATES; QUALITATIVE RESEARCH
8 CRITICAL QUANTITATIVE INTERSECTIONALITY; EQUALITY; EQUITY; GENDER; HIERARCHICAL LINEAR MODEL; HIGHER EDUCATION; LEARNING; PHYSICS; RACE
9 BLACK MEN; CRITICAL RACE THEORY; PSYCHOLOGICAL HEALTH AND WELL-BEING; RACISM; RESPECTABILITY POLITICS; STEM DOCTORAL PROGRAMS
10 <NA>
11 BLACK; MINORITY; WOMEN; STEM; COMMUNITY COLLEGE; TRANSFER; RACISM; SEXISM
12 COLLEGE SCIENCE FACULTY; COLOR-BLIND RACISM; CRITICAL RACIAL CONSCIOUSNESS; RACIALLY MINORITIZED STUDENTS
13 <NA>
14 <NA>
15 COUNTERSPACE; MENTAL HEALTH; PERSISTENCE; STEM; WOMEN OF COLOR
16 WOMEN OF COLOR; ENGINEERING EDUCATION; HIGHER EDUCATION; SALARY; IDENTITY TAXATION; STEREOTYPE MANAGEMENT
17 WHITE SUPREMACY; ANTI-RACISM; STEM; WOMEN; ACTIVISM
KeywordsPlus
1 COLLEGE-STUDENTS; AFRICAN-AMERICAN; GENDER; RACE; PERCEPTIONS; EXPERIENCES; WOMEN
2 CRITICAL RACE THEORY; STEREOTYPE THREAT; SCIENCE; EXPERIENCES; EDUCATION; MICROAGGRESSIONS; MATHEMATICS; DIVERSITY; STUDENTS; COLOR
3 EXPERIENCES; PERSISTENCE; STUDENTS
4 AFRICAN-AMERICAN; HIGHER-EDUCATION; IDENTITY; RACE; MICROAGGRESSIONS; MATHEMATICS; EXPERIENCES; CAREERS; IMPACT; WOMEN
5 AFRICAN-AMERICAN STUDENTS; CRITICAL RACE THEORY; PREDOMINANTLY WHITE; GENDER-DIFFERENCES; COLLEGE-STUDENTS; EXPERIENCES; CLIMATE; COLOR; STEREOTYPE; OPPORTUNITIES
6 MATHEMATICS EDUCATION; COLOR; BLACK; RACE; SCIENCE; IDENTITY; STUDENTS; HEALTH; WOMEN; EXPERIENCES
7 DOUBLE BIND; SCIENCE; WOMEN; RACE
8 GENDER
9 RACE; COLLEGE; MASCULINITY; PERSISTENCE; EDUCATION; WOMEN
10 COLORADO LEARNING ATTITUDES; SELF-EFFICACY; WOMEN; GENDER; MODEL; IMPUTATION; BELIEFS
11 FEMALE TRANSFER STUDENTS; HIGHER-EDUCATION; TRANSFER SHOCK; ACADEMIC-PERFORMANCE; CHILLY CLIMATE; DOUBLE BIND; COLOR; GENDER; SCIENCE; UNDERGRADUATE
12 STUDENTS; WOMEN; RACE; UNDERGRADUATE; EDUCATION; RACISM; GAPS; ACHIEVEMENT; PERFORMANCE; PERSISTENCE
13 CRITICAL RACE THEORY; MATHEMATICS; PERSISTENCE; IDENTITIES; BLACK; OPPORTUNITIES; PERCEPTIONS; EXPERIENCES; EDUCATION; IDEOLOGY
14 HIGHER-EDUCATION; RACISM; COLOR; MATHEMATICS; STUDENTS; EXPERIENCES; IDEOLOGY; CLIMATE; WOMEN
15 CAMPUS RACIAL CLIMATE; CRITICAL RACE THEORY; HIDDEN CURRICULUM; BLACK-WOMEN; EXPERIENCES; SCIENCE; WHITE; MICROAGGRESSIONS; EDUCATION; IDENTITY
16 GENDER STEREOTYPES; STEM; RACE; BLACK; PREJUDICE; PROMOTION; EMOTION; EQUITY; WOMAN; NEED
17 EDUCATION; STUDENTS; SCIENCE; COLOR
DOI Year LCS GCS
1 10.1037/a0038676 2015 5 88
2 10.3102/0002831216676572 2016 14 230
3 10.1002/tea.21249 2016 2 62
4 10.1177/2332858418816658 2018 4 61
5 10.1186/s40594-020-00241-4 2020 3 66
6 10.3102/0013189X20972718 2020 10 222
7 10.1186/s40594-020-00250-3 2020 2 23
8 10.1002/tea.21584 2020 1 55
9 10.1111/socf.12724 2021 1 9
10 10.1103/PhysRevPhysEducRes.17.010116 2021 1 32
11 10.1186/s40594-022-00334-2 2022 2 14
12 10.1002/tea.21775 2022 3 22
13 10.1080/10508406.2022.2073233 2022 3 8
14 10.1187/cbe.22-06-0104 2023 2 18
15 10.1002/tea.21798 2023 1 10
16 10.1080/01419870.2022.2159474 2023 1 5
17 10.1080/00221546.2023.2265285 2024 1 2Top token frequencies
# Top token frequencies
top_tokens <- toks_clean %>%
tokens_group() %>%
dfm() %>%
textstat_frequency(n = 20)
top_tokens %>% # top tokens from abstracts
filter(feature != "research") %>%
filter(feature != "study") %>%
filter(feature != "article") %>%
filter(feature != "also") %>%
filter(feature != "can") feature frequency rank docfreq group
1 stem 343 1 145 all
2 racism 330 2 186 all
3 black 323 3 88 all
4 health 249 4 67 all
5 students 208 5 60 all
6 social 202 6 98 all
8 racial 192 8 93 all
10 women 176 10 54 all
11 white 174 11 79 all
12 experiences 160 12 80 all
14 education 127 14 67 all
17 data 115 17 78 all
18 indigenous 114 18 33 all
19 science 113 19 50 all
20 race 109 20 66 all