Codes to generate analysis and figures for Quantifying Institutional Gender Inequality in Contemporary Visual Art
To generate all results and figures of the main paper, run ./remove_birth.sh
.
In this shell script, it contains the following steps:
python 01-00-data_processing.py -t 0.6 -f
: create merged dataframes (shows_with_person_with_gender,
career_start_end, sales_with_person), filter data and print out basic information.
python 01-01-data_selection.py -t 0.6 -f -y 1990 -a
: select data based on the career start year we select, create
corresponding selected dataframe. Generate Figure 1.
python 03-00-gender_preference_assign.py -t 0.6 -f -y 1990
: assign institution to gender inequality category
python 03-01-gender_preference_stats.py -t 0.6 -f -y 1990 -p neutral -a
and
python 03-01-gender_preference_stats.py -t 0.6 -f -y 1990 -p balance -a
:
Basic statistics (count) of gender inequality category under gender-neutral and gender-balanced criteria. Generate
Figure 2b, 2c 2d, Figure 3a, 3b.
python 04-artist_career_gender_preference.py -t 0.6 -f -y 1990 -p neutral -n 10 -a
: assign co-exhibition gender to
artist, and further analysis. Generate Figure 5
python 05-00-sales_data_prep.py -t 0.6 -f -y 1990 -p neutral -n 10 -a
: prepare sales data and output auction bias.
Generate Figure 6a.
python 05-03-logit.py -t 0.6 -f -y 1990 -p neutral -n 10 -a
: logistic regression model. Generate Table 2, Table 3 and
Figure 6b, 6c.
python 03-02-gender_preference_ins_cases.py 1990
python 03-03-gender_preference_scatter_boundary.py 1990
python 06-network_viz.py 1990
generates the gml file of the network and we further did visualization using gephi
python 03-00-gender_preference_assign_power_analysis.py -f -p neutral
python 03-00-gender_preference_assign_power_analysis.py -f -p balance
Results are printed in the command line.
# process and get Men Artists/Women Artists/Ratio
python 01-00-data_processing.py --threshold 0.6 --remove_birth
python 01-00-data_processing.py --threshold 0.8 --remove_birth
python 01-00-data_processing.py --threshold 0.9 --remove_birth
# select, and get Selected Men Artists/Selected Women Artists/Ratio
python 01-01-data_selection.py -t 0.6 --remove_birth -y 1990
python 01-01-data_selection.py -t 0.8 --remove_birth -y 1990
python 01-01-data_selection.py -t 0.9 --remove_birth -y 1990
Results are printed in the command line.
For Men Exhibitions/Women Exhibitions/Exhibition Ratio
python 01-01-data_selection.py -t 0.6 --remove_birth -y 1990
python 01-01-data_selection.py -t 0.8 --remove_birth -y 1990
python 01-01-data_selection.py -t 0.9 --remove_birth -y 1990
For Man-overrepresented Institutions/Woman-overrepresented Institutions/Gender-neutral Institutions
python 03-00-gender_preference_assign.py -t 0.9 --remove_birth
Results are printed in the command line.
cd SI_codes
python 03-00-gender_preference_assign_bonferroni.py --remove_birth`
python 03-01-gender_preference_stats_bonferroni.py --remove_birth -p neutral
python 03-01-gender_preference_stats_bonferroni.py --remove_birth -p balance
python generate_neighborhood_csv.py -f -p neutral
python generate_neighborhood_csv.py -f -p balance
The codes for panel e and f is in SI_codes/MultiscaleMixing/Art Gender Network Multiscale.ipynb
cd SI_codes
python 02-01-time_trend.py -f
cd SI_codes
python career_start_year_stability.py
Run python 04-artist_career_gender_preference.py -t 0.6 --no-remove_birth -y 1990 -p balance -n 10 --no-save
Run all scripts under SI_codes/solo_exhibitions
python 05-03-logit.py -t 0.6 --no-remove_birth -y 1990 -p neutral -n 10 --no-save
python 03-01-gender_preference_stats.py -t 0.6 -f -y 1990 -p neutral
python 03-01-gender_preference_stats.py -t 0.6 -f -y 1990 -p balance
cd SI_codes
python 03-01-gender_preference_expert_grade.py -p neutral
python 03-01-gender_preference_expert_grade.py -p balance
cd SI_codes
python 03-00-gender_preference_assign_no_30_filter.py -f
python 03-01-gender_preference_stats_no_30_filter.py -f -p neutral
python 03-01-gender_preference_stats_no_30_filter.py -f -p balance
Panel a, b: python 04-artist_career_gender_preference.py --remove_birth -p neutral -n 15
Panel c, d: python 04-artist_career_gender_preference.py --remove_birth -p neutral -n 20
Run no_remove_birth.sh
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