Analyse gender in music psychology

T. Eerola, 1/8/2025

Gender analysis of the music psychology journals since 2000 (Musicae Scientiae, Psychology of Music, Music Perception, Journal of New Music Research, and Music & Science).

Required libraries

Load and preprocess data

source('scripts/load_scopus_datasets.R') # Creates full_names dataset

Entries in the merged database: 3926 Entries in the author-expanded database: 9922 Entries in the author-expanded database after filtering editorials etc: 9662 Entries in the author-expanded database after filtering empty: 9651

source('scripts/attribute_country.R') # Add country affiliations

Entries in the author-expanded database after author affiliations: 9630

source('scripts/attribute_gender.R') # Adds gender attributions (precalculated with API)

Gender	n	percentage
ambiguous	32	0.4
female	3701	40.6
male	5365	58.8
unknown	27	0.3
Total	9125	100.1

Entries in the author-expanded database after filtering books/unattributed: 9066

source('scripts/name_gender_fixes.R') # Fixes the gender attribution of some names manually

Manual fixes

source('scripts/create_keys.R') # Add keys for authors and study+author

Single authors count: 778 Single + last authors count: 3373 Unique authors count: 5312 Unique countries count: 63

source('scripts/clean_citations_and_OA.R') # Process citations and OA status
source('scripts/attribute_gender_diagnostics.R') # Process citations and OA status

Unique studies: 3373

Unique Entries as studies and author combinations: 9066

Number of rows: 9066

JOURNAL	n	percentage
Journal of New Music Research	1496	16.5
Music Perception	1845	20.4
Music and Science	900	9.9
Musicae Scientiae	1524	16.8
Psychology of Music	3301	36.4
Total	9066	100.0

Prop. over .90 conf. = 0.892786234281932 /nProp. over .95 conf. = 0.866644606221046 /nProp. under .55 conf. = 0.00981689830134569 /nProp. under .55 conf. = 89 /n/n ***

Gender	M	Md
female	0.96	1
male	0.97	1

Gender	M	Md	LCI	UCI
female	19.81	9	18.73	20.88
male	22.24	10	21.24	23.24

A tibble: 1 × 10

estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high 1 -2.43 19.8 22.2 -3.25 0.00114 8417. -3.90 -0.967 # ℹ 2 more variables: method , alternative # A tibble: 1 × 4 statistic p.value method alternative
1 9392423 0.0000616 Wilcoxon rank sum test with continuity correc… two.sided

Gender Distribution
est	lwr.ci	upr.ci
0.402	0.392	0.413
0.598	0.587	0.608

source('scripts/export_gender_data.R')

Summarise

source('scripts/summarise_gender.R') # different summaries

Gender	n	percentage
female	3647	40.2
male	5419	59.8
Total	9066	100.0

Number of coauthors:

median: 2 mean: 2.688 sd: 1.77 max: 34

Quantify

source('scripts/quantify_authorship.R') # use Odds

name	Odds_ratio	CI_low	CI_high
Single	1.11	0.95	1.29
First	1.41	1.28	1.54
Coauthor	1.00	0.91	1.09
Last	0.73	0.67	0.81

[1] “5-year growth rate of female authorships:”

Type	AAGR
First	12.15
Coauthor	-10.81
Last	-9.89

source('scripts/citations.R')           # Citations and gender
print(knitr::kable(citestats_all, digits = 2, caption = 'Citations across all authors'))

Citations across all authors
Gender	Md	M	Q75	CI_lower	CI_upper
female	9	19.81	22	9	10
male	10	22.24	27	9	10

print(knitr::kable(stats_all))

statistic	p.value	parameter	method
16.05391	6.16e-05	1	Kruskal-Wallis rank sum test

source('scripts/open_access.R')         # OA and gender
print(knitr::kable(author_OA, digits = 2, caption = 'Open access across all authors'))

Open access across all authors
Odds_ratio	CI	CI_low	CI_high	Type
1.49	0.95	1.29	1.73	First
1.06	0.95	0.92	1.23	Co
0.86	0.95	0.72	1.02	Last

Geographical

source('scripts/country_table.R') # OK

source('scripts/visualise_gender.R') # OK

Top countries

Female author odds ratios across continents.
Continent	N	Odds_ratio	CI_low	CI_high
Europe	4985	0.82	0.75	0.89
Americas	2356	1.12	1.02	1.23
Asia	931	0.89	0.78	1.02
Oceania	742	1.66	1.43	1.93
Africa	52	0.91	0.52	1.59

Keywords and gender

source('scripts/keyword_analysis.R')