This repository contains the code to replicate the analysis from the paper "Moving On - Investigating Inventors' Ethnic Origins Using Supervised Learning"

Overview

Replication Code for 'Moving On' - Investigating Inventors' Ethnic Origins Using Supervised Learning

This repository contains the code to replicate the paper Moving On - Investigating Inventors' Ethnic Origins Using Supervised Learning.

Repository Structure

Datasets that were created in this analysis can be found in the folder 00_data_and_model. The trained and tuned LSTM classification model used for the analysis in this paper is stored in this folder as well and can be accessed under 00_data_and_model/model/name_origin_lstm.h5. The folder 01_create_training_dataset contains replication files used to construct the dataset of labeld names used to train the LSTM classification model. 02_model_training features the code to train the LSTM classifier. Lastly, the code for the descriptive analysis (using a random subsample of the paper'sb dataset) can be found in the folder 03_inventor_composition_analysis

Dependencies

Python (3.7)

  • joblib==1.0.1
  • matplotlib==3.3.1
  • numpy==1.19.2
  • pandas==1.1.3
  • pyreadr==0.3.5
  • scikit-learn==0.23.2
  • scipy==1.4.1
  • tensorflow==2.2.0
  • xgboost==0.90

Installing a virtual environment using the environment.yml or requirements.txt files is recommended.

R (4.0.1)

  • tidyverse
  • data.table
  • reticulate
  • tensorflow
  • keras
  • stringi
  • jsonlite
  • countrycode
  • viridis

References & Contact

Niggli, M. (2022), 'Moving On' -- Investigating Inventors' Ethnic Origins Using Supervised Learning, arXiv:2201.00578

If you have questions, please contact [email protected].

Owner
Matthias Niggli
Matthias Niggli
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