This repository houses the code to fully reproduce the study by Lietz et al. (forthcoming). Read the preprint here.
The code is developed for the Python and package versions specified in the environment file. To guarantee reproducibility, you can set up a local environment by following these steps:
- Install the Anaconda Distribution
- Download the environment file into your user directory
- In your user directory, run this command to set up a local environment:
conda env create -f environment.yml
- Activate this environment to work with it:
conda activate inequality
You can also run this code in the cloud by clicking on this button:
A docker image is here.
To reproduce the study, first execute the preprocessing notebook. This will write files. Once these files are written, you can execute the other notebooks to produce all figures and tables in the paper.
This is the study that is getting reproduced:
@article{lietz_individual_forthcoming,
author={Haiko Lietz and Mohsen Jadidi and Daniel Kostic and Milena Tsvetkova and Claudia Wagner},
title={Individual and gender inequality in computer science: {A} career study of cohorts from 1970 to 2000},
journal={Quantitative Science Studies},
year={forthcoming}
}
This is the data used in the study:
@misc{lietz_computer_2023,
author = {Haiko Lietz and Mohsen Jadidi and Daniel Kostic and Claudia Wagner},
title = {Computer Science (1970-2014)},
year = {2023},
howpublished = {GESIS, K{\"o}ln. Data file version 1.0.0, https://doi.org/10.7802/2642},
doi = {10.7802/2642}
}
Please cite this code repository as follows:
@misc{kostic_inequality_2023,
author = {Daniel Kostic and Haiko Lietz and Mohsen Jadidi and Claudia Wagner},
title = {Individual and gender inequality in computer science: {A} career study of cohorts from 1970 to 2000},
year = {2023},
publisher = {GitHub},
howpublished = {\url{https://github.com/gesiscss/inequality/}}
}
Creative Commons Attribution 4.0 International (CC BY 4.0)