#install.packages("openalexR")
library(openalexR)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.
To that end, good research requires knowledge of the peer-reviewed literature.
OpenAlex
We will use OpenAlex to support your literature identification process. OpenAlex is a comprehensive open catalog of the global research system that can help you find relevant publications for your research.
Once you are set up in R from part 1, we’ll start working with the code below:
# Search for works related to your social justice topic
works_search <- oa_fetch(
entity = "works",
title.search = c("BlackCrit", "youth"),
from_publication_date = "2026-03-01",
options = list(sort = "cited_by_count:desc"),
verbose = TRUE
)Requesting url:
<https://api.openalex.org/works?filter=title.search%3ABlackCrit%7Cyouth%2Cfrom_publication_date%3A2026-03-01&sort=cited_by_count%3Adesc>
ℹ Getting 14 pages of results with a total of 2613 records...
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⠴ Converting [2613/2613] ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% ETA: 0s# Display the top results
works_search |>
head(10) |>
show_works() |>
knitr::kable()| id | display_name | first_author | last_author | is_oa | top_concepts |
|---|---|---|---|---|---|
| W578333627 | Youth Training and the Search for Work | Denis Gleeson | NA | FALSE | Training (meteorology), Work (physics) |
| W7133883343 | Faith, flight, and futures: religion and the Japa migration of Nigerian youth | Ntongha Eni Ikpi | Dodeye Uduak Williams | FALSE | Ethnic group, Immigration, Ethnography |
| W7133782334 | Faith, governance, and exodus: exploring the impact of religion and politics on youth migration in Africa | Dodeye Uduak Williams | NA | FALSE | Politics, Ethnic group, Immigration |
| W7135378591 | Vaping as Captured Homeostasis - Adverse Events, Trauma Response, and the Case for Stewardship in Australian Youth Nicotine Policy | John Richard Smith | SHAI/HATI | TRUE | Enforcement, Harm, Framing (construction) |
| W7134069619 | Transnational youth and social mobility: the role of family financial support in unsettled lives | Alexandra Lee | Loretta Baldassar | TRUE | Social support, Remittance, Politics |
| W7133207944 | Within-Person Association Between Daily Screen Use and Sleep in Youth | Matthew Bourke | George Thomas | FALSE | Bedtime, Sleep (system call), Association (psychology) |
Web of Science (WoS)
We can also use the WoS to conduct a full systematic literature review with the Quanteda package.
Web of Science (WoS) is a comprehensive and highly respected citation database used for scholarly research. It is one of the main databases used to identify and compare collections of citations and bibliographic sources. Given the conceptual replication method selected for this study, we utilized the Web of Science Core Collection of bibliometric data. This decision provided us with an opportunity to understand differences across disciplinary boundaries, despite the increasingly interdisciplinary nature of research on racism in STEM. Prior studies have analyzed differences between Google Scholar and WoS. Unlike Google Scholar, the Web of Science Core Collection offers access to multiple citation indexes, covering a wide range of academic disciplines and publication types, making it an ideal resource for data collection and analysis.
For this section, I will walk us through a sample study conducted with two graduate students on notions of racism in STEM.
[insert table: Results of title keyword search]
| Keyword | EBSCO | Google Scholar | Scopus | Web of Science |
|---|---|---|---|---|
| anti-racism | 381 | 3,420 | 685 | 592 |
| anti-racist | 427 | 3,620 | 826 | 685 |
| race | 44,057 | ~313,000 | 65,203 | 69,537 |
| racial | 24,467 | ~212,000 | 39,930 | 41,527 |
| racialization | 519 | 2,740 | 1,108 | 1,077 |
| racialized | 958 | ~5,600 | 1,863 | 2,120 |
| racism | 8,669 | ~99,500 | 11,418 | 10,674 |
| racist | 1,965 | ~15,100 | 2,170 | 1,906 |
Systematic review techniques offer a structured approach to synthesizing research literature. New computational methods have significantly advanced systematic reviews, helping with more pre-defined protocols outlining the methodologies that should be undertaken for reproducibility. These approaches employ a comprehensive, detailed search strategy across multiple databases and sources to find relevant studies. Systematic reviews also utilize specific, pre-defined inclusion and exclusion criteria for studies, whereas more critical reviews may rely on subjective or seemingly unclear selection criteria due to content- or disciplinary-specific knowledge. This often ignored but critical aspect of systematic reviews requires a rigorous assessment of its contribution to and limitations around study quality and risk of bias.
## set up, load libraries
library(dplyr)
library(readtext)
library(tidyverse)
library(here)
library(gt)
library(ggplot2)
library(dplyr)
library(knitr)
library(readr)
library(kableExtra)
library(bibliometrix)
library(tidyverse)
library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)
library(quanteda)
library(stringr)
library(tidytext)
library("quanteda.textmodels")
library("quanteda.textstats")
library("quanteda.textplots")
require(quanteda.corpora)
here::i_am("part02.qmd")RESEARCH QUESTIONS
What is the intellectual and conceptual structure of research on racism in science, technology, engineering, and mathematics (STEM)?
How are notions of racism in the research on STEM distributed across different racialized social systems?
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
FINDINGS
Converting your wos collection into a bibliographic dataframe
Done!
Generating affiliation field tag AU_UN from C1: Done![1] 320 69
MAIN INFORMATION ABOUT DATA
Timespan 2014 : 2025
Sources (Journals, Books, etc) 256
Documents 320
Annual Growth Rate % -18.11
Document Average Age 4.95
Average citations per doc 12.56
Average citations per year per doc 1.817
References 18413
DOCUMENT TYPES
article 295
article; early access 19
article; proceedings paper 1
editorial material; early access 1
review; early access 4
DOCUMENT CONTENTS
Keywords Plus (ID) 703
Author's Keywords (DE) 1053
AUTHORS
Authors 889
Author Appearances 912
Authors of single-authored docs 134
AUTHORS COLLABORATION
Single-authored docs 138
Documents per Author 0.36
Co-Authors per Doc 2.85
International co-authorships % 9.375
Annual Scientific Production
Year Articles
2014 9
2015 11
2016 14
2017 13
2018 14
2019 22
2020 21
2021 41
2022 47
2023 57
2024 70
2025 1
Annual Percentage Growth Rate -18.11
Most Productive Authors
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 1.00
Top manuscripts per citations
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 4.55
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 USA 192 0.6076 179 13 0.0677
2 UNITED KINGDOM 27 0.0854 24 3 0.1111
3 CANADA 20 0.0633 17 3 0.1500
4 AUSTRALIA 13 0.0411 10 3 0.2308
5 CHINA 9 0.0285 9 0 0.0000
6 BRAZIL 7 0.0222 5 2 0.2857
7 GERMANY 4 0.0127 4 0 0.0000
8 KOREA 4 0.0127 4 0 0.0000
9 SOUTH AFRICA 4 0.0127 4 0 0.0000
10 SWEDEN 4 0.0127 3 1 0.2500
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 USA 2754 14.34
2 UNITED KINGDOM 304 11.26
3 CANADA 238 11.90
4 SWEDEN 178 44.50
5 BRAZIL 82 11.71
6 ESTONIA 78 78.00
7 AUSTRALIA 64 4.92
8 ISRAEL 60 30.00
9 ECUADOR 58 58.00
10 CHINA 45 5.00
Most Relevant Sources
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 4
Most Relevant Keywords
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 17Descriptive (performance) analysis
Summary of the data set and documents.
Publication-related metrics
Main information
Main information about the collection.
S[2] # Main information$MainInformationDF
Description Results
1 MAIN INFORMATION ABOUT DATA
2 Timespan 2014:2025
3 Sources (Journals, Books, etc) 256
4 Documents 320
5 Annual Growth Rate % - 18.11
6 Document Average Age 4.95
7 Average citations per doc 12.56
8 Average citations per year per doc 1.817
9 References 18413
10 DOCUMENT TYPES
11 article 295
12 article; early access 19
13 article; proceedings paper 1
14 editorial material; early access 1
15 review; early access 4
16 DOCUMENT CONTENTS
17 Keywords Plus (ID) 703
18 Author's Keywords (DE) 1053
19 AUTHORS
20 Authors 889
21 Author Appearances 912
22 Authors of single-authored docs 134
23 AUTHORS COLLABORATION
24 Single-authored docs 138
25 Documents per Author 0.36
26 Co-Authors per Doc 2.85
27 International co-authorships % 9.375
28 Publications by year
S[3] # Article count by year$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
# Save the plot as a high-resolution PNG
#ggsave("plots/publications_by_year.png", plot = pubs_by_year, width = 10, height = 8, units = "in", dpi = 300, bg = "white")Most cited papers
S[6] # Most cited papers$MostCitedPapers
Paper DOI TC TCperYear NTC
1 MCGEE EO, 2016, AM EDUC RES J 10.3102/0002831216676572 230 20.91 4.99
2 MCGEE EO, 2020, EDUC RESEARCHER 10.3102/0013189X20972718 222 31.71 8.05
3 CHAVEZ-DUEÑAS NY, 2019, AM PSYCHOL 10.1037/amp0000289 187 23.38 8.18
4 FUENFSCHILLING L, 2018, RES POLICY 10.1016/j.respol.2018.02.003 155 17.22 4.76
5 MCCOY DL, 2015, J DIVERS HIGH EDUC 10.1037/a0038676 88 7.33 5.63
6 KIIK L, 2016, EURASIAN GEOGR ECON 10.1080/15387216.2016.1198265 78 7.09 1.69
7 SLAUGHTER-ACEY JC, 2016, ANN EPIDEMIOL 10.1016/j.annepidem.2015.10.005 76 6.91 1.65
8 MCGEE EO, 2019, TEACH COLL REC NA 68 8.50 2.97
9 LEE MGJ, 2020, INT J STEM EDUC 10.1186/s40594-020-00241-4 66 9.43 2.39
10 HARTMAN TK, 2021, SOC PSYCHOL PERS SCI 10.1177/1948550620978023 63 10.50 4.55Main sources
S[9] # Main sources (journals)$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 4Keywords and Keywords Plus
Keyword 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)# Plot a historical co-citation network
net <- histPlot(histResults, n=15, size = 8, labelsize=4)
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 2# LEXICAL PATTERNS
# keywords in context
M4_abstract <- corpus(M4$AB)
# M4_abstract
toks_M4_abstract <- corpus_subset(M4_abstract) %>%
tokens()
toks <- toks_M4_abstract
toks_clean <- tokens(toks,
remove_punct = TRUE,
remove_numbers = TRUE) %>%
tokens_remove(stopwords("english"))Top 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