Analysis of gender distribution of authors of journals articles in musicology

T. Eerola, 29/9/2025, update 30/1/2026 after Dana’s manual edits.

This is the first pass to analyse gender distribution of the authors in musicology journals since 2000. The dataset contains 15 journals in musicology that were selected from various Scimago list minus music psychology journals. The journal selection can be altered, but currently these journals are included (primary sub-discipline in brackets):

  • Journal of the American Musicological Society (Musicology)
  • Music and Letters (Musicology)
  • Journal of Musicology (Musicology)
  • Nineteenth-Century Music Review (Musicology)
  • Nineteenth Century Music (Musicology)
  • Music Theory Spectrum (Music Analysis)
  • Ethnomusicology (Ethnomusicology)
  • Ethnomusicology Forum (Ethnomusicology)
  • Music Analysis (Music Analysis)
  • Popular Music and Society (Popular Music)
  • Popular Music (Popular Music)
  • Journal of Popular Music Studies (Popular Music)
  • Journal of World Popular Music (Popular Music)
  • Journal of Popular Music Education (Popular Music)
  • Popular Music History (Popular Music)

This amounted to 4,089 authors.

At the moment, the data is nearly complete although 22% of the affiliations are missing.

Load and preprocess data

source('scripts/load_scopus_datasets.R') # Creates full_names dataset
source('scripts/attribute_country.R') # Add country affiliations
source('scripts/attribute_gender.R') # Adds gender attributions (precalculated with API)
source('scripts/create_keys.R') # Add keys for authors and study+author
source('scripts/clean_citations_and_OA.R') # Process citations and OA status
source('scripts/attribute_gender_diagnostics.R') # Process citations and OA status
source('scripts/export_gender_data.R')

Check manual corrections

Changed first name: 58

Changed last name: 2

Missing affiliations: original data: 1055

Missing affiliations: curated data: 887

Changed gender attribution: 7

Missing affiliations by journal
JOURNAL N N_missing pct_missing
Ethnomusicology 295 13 4.41
Ethnomusicology Forum 207 0 0.00
Journal of Musicology 228 120 52.63
Journal of Popular Music Education 202 1 0.50
Journal of Popular Music Studies 570 31 5.44
Journal of World Popular Music 172 0 0.00
Journal of the American Musicological Society 148 44 29.73
Music Analysis 237 149 62.87
Music Theory Spectrum 254 196 77.17
Music and Letters 253 35 13.83
Nineteenth Century Music 97 78 80.41
Nineteenth-Century Music Review 210 21 10.00
Popular Music 372 58 15.59
Popular Music History 71 1 1.41
Popular Music and Society 773 140 18.11

Summarise

source('scripts/summarise_gender.R') # different summaries
Gender N pct
female 1425 0.3485812
male 2663 0.6514188
Gender M_coauthors Md_coauthors SD_coauthors
female 1.461754 1 1.0133122
male 1.399549 1 0.9563893 

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_text()`).

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_text()`).

Number of coauthors:

median: 1

mean: 1.163

sd: 0.548

max: 8

[1] “How many authors can be attributed?” [1] 0.7228474

Warning: Some values were not matched unambiguously: UK, UNITED STATES
Continent n female male female_prop male_prop
Africa 48 17 31 0.3541667 0.6458333
Americas 1450 564 886 0.3889655 0.6110345
Asia 186 52 134 0.2795699 0.7204301
Europe 1029 370 659 0.3595724 0.6404276
Oceania 238 85 153 0.3571429 0.6428571
Unknown 1137 337 800 0.2963940 0.7036060
All 4088 1425 2663 0.3485812 0.6514188

Quantify roles

Using the odds ratio but note that there are relatively few coauthors in the data.

source('scripts/quantify_authorship.R') # use Odds
Odds_ratio CI CI_low CI_high name
0.83 0.95 0.72 0.96 Single
1.26 0.95 1.02 1.56 First
1.06 0.95 0.77 1.46 Coauthor
1.12 0.95 0.91 1.39 Last

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

Type AAGR
First -14.41
Coauthor -15.45
Last 18.28 
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 2 5.86 7 2 3
male 3 6.70 8 2 3 
print(knitr::kable(stats_all))
statistic p.value parameter method
5.001684 0.0253227 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
1.14 0.95 0.95 1.37 First
1.10 0.95 0.54 2.24 Co
0.95 0.95 0.60 1.51 Last

Quantify citations and gender

Probably not relevant quantity for these disciplines, but just to see if there are differences.

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 2 5.86 7 2 3
male 3 6.70 8 2 3 
print(knitr::kable(stats_all))
statistic p.value parameter method
5.001684 0.0253227 1 Kruskal-Wallis rank sum test

OA and gender?

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
1.14 0.95 0.95 1.37 First
1.10 0.95 0.54 2.24 Co
0.95 0.95 0.60 1.51 Last

Geographical distributions

Do some basic summaries of geographical distributions. Start with countries with most publications overall.

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

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

Keyword analysis

TBD.

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