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## Core latex/pdflatex auxiliary files:
*.aux
*.lof
*.log
*.lot
*.fls
*.out
*.toc
*.fmt
*.fot
*.cb
*.cb2
.*.lb

## Intermediate documents:
*.dvi
*.xdv
*-converted-to.*
# these rules might exclude image files for figures etc.
# *.ps
# *.eps
# *.pdf

## Generated if empty string is given at "Please type another file name for output:"
.pdf

## Bibliography auxiliary files (bibtex/biblatex/biber):
*.bbl
*.bcf
*.blg
*-blx.aux
*-blx.bib
*.run.xml

## Build tool auxiliary files:
*.fdb_latexmk
*.synctex
*.synctex(busy)
*.synctex.gz
*.synctex.gz(busy)
*.pdfsync

## Auxiliary and intermediate files from other packages:
# algorithms
*.alg
*.loa

# achemso
acs-*.bib

# amsthm
*.thm

# beamer
*.nav
*.pre
*.snm
*.vrb

# changes
*.soc

# cprotect
*.cpt

# elsarticle (documentclass of Elsevier journals)
*.spl

# endnotes
*.ent

# fixme
*.lox

# feynmf/feynmp
*.mf
*.mp
*.t[1-9]
*.t[1-9][0-9]
*.tfm

#(r)(e)ledmac/(r)(e)ledpar
*.end
*.?end
*.[1-9]
*.[1-9][0-9]
*.[1-9][0-9][0-9]
*.[1-9]R
*.[1-9][0-9]R
*.[1-9][0-9][0-9]R
*.eledsec[1-9]
*.eledsec[1-9]R
*.eledsec[1-9][0-9]
*.eledsec[1-9][0-9]R
*.eledsec[1-9][0-9][0-9]
*.eledsec[1-9][0-9][0-9]R

# glossaries
*.acn
*.acr
*.glg
*.glo
*.gls
*.glsdefs

# gnuplottex
*-gnuplottex-*

# gregoriotex
*.gaux
*.gtex

# htlatex
*.4ct
*.4tc
*.idv
*.lg
*.trc
*.xref

# hyperref
*.brf

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*-concordance.tex
# TODO Comment the next line if you want to keep your tikz graphics files
*.tikz
*-tikzDictionary

# listings
*.lol

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*.idx
*.ilg
*.ind
*.ist

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*.maf
*.mlf
*.mlt
*.mtc[0-9]*
*.slf[0-9]*
*.slt[0-9]*
*.stc[0-9]*

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_minted*
*.pyg

# morewrites
*.mw

# nomencl
*.nlg
*.nlo
*.nls

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*.pax

# pdfpcnotes
*.pdfpc

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*.sagetex.sage
*.sagetex.py
*.sagetex.scmd

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*.wrt

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*.sout
*.sympy
sympy-plots-for-*.tex/

# pdfcomment
*.upa
*.upb

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*.pytxcode
pythontex-files-*/

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*.loe

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*.dpth
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*.tdo

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*.xmpi

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*.xdy

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*.xyc

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# Latexian
TSWLatexianTemp*

## Editors:
# WinEdt
*.bak
*.sav

# Texpad
.texpadtmp

# Kile
*.backup

# KBibTeX
*~[0-9]*

# auto folder when using emacs and auctex
./auto/*
*.el

# expex forward references with \gathertags
*-tags.tex

# standalone packages
*.sta

# generated if using elsarticle.cls
*.spl

**/.ipynb_checkpoints/**
/.idea
**/__pycache__/**
/venv/
25 changes: 25 additions & 0 deletions README.md
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[![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.4070769.svg)](http://dx.doi.org/10.5281/zenodo.4070769)

# Complex Societies and the Growth of the Law

This repository contains the source of the main paper (unlayouted final version submitted to the publisher) and the supplementary information (SI), along with further graphics and Jupyter notebooks to reproduce the results, for the following publication:

Daniel Martin Katz, Corinna Coupette, Janis Beckedorf, and Dirk Hartung, Complex Societies and the Growth of the Law, *Sci. Rep.* **10** (2020), [https://doi.org/10.1038/s41598-020-73623-x](https://doi.org/10.1038/s41598-020-73623-x)

Related Repositories:
- [Legal Data Preprocessing](https://github.com/QuantLaw/legal-data-preprocessing) ([Publication Release](http://dx.doi.org/10.5281/zenodo.4070773))
- [Legal Data Clustering](https://github.com/QuantLaw/legal-data-clustering) ([Publication Release](http://dx.doi.org/10.5281/zenodo.4070775))

Related Data: [Preprocessed Input Data](http://dx.doi.org/10.5281/zenodo.4070767)

## Setup

1. It is assumed that you have Python 3.7 installed. (Other versions are not tested.)
2. Set up a virtual environment and activate it. (This is not required but recommended.)
3. Install the required packages `pip install -r requirements.txt`.

## Usage

- To explore the notebooks, start `jupyter lab` from the terminal.
The prefixes of the notebook names suggest an order in which they may be explored (i.e., notebooks prefixed 01-05 first, then notebooks prefixed si_01-si_05).
- To generate the PDFs of the main paper (unlayouted final version submitted to the publisher) and the SI, execute `latexmk -pdf main-scirep` and `latexmk -pdf si-scirep` in the directory `journal_version`.
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\begin{tabular}{llrrrrrrr}
\toprule
& & \multicolumn{1}{p{18mm}}{\centering Slope} & \multicolumn{1}{p{18mm}}{\centering Intercept} & \multicolumn{1}{p{10mm}}{\centering P-value} & \multicolumn{1}{p{16mm}}{\centering Correlation\\coefficient ($r$)} & \multicolumn{1}{p{10mm}}{\centering $r^2$} & \multicolumn{1}{p{16mm}}{\centering Standard\\error} & \multicolumn{1}{p{18mm}}{\centering Mean value} \\
\multicolumn{1}{p{14mm}}{\centering Country\\code} & \multicolumn{1}{p{14mm}}{\centering Cluster\\family} & & & & & & & \\
\midrule
US & 3 in 2015 & 53488.68 & 1410483.92 & 0.00 & 0.99 & 0.98 & 1504.06 & 2052348.04 \\
& 0 in 2010 & 20618.14 & 440351.10 & 0.00 & 0.95 & 0.90 & 1423.24 & 687768.80 \\
& 3 in 2011 & 18660.65 & 611768.54 & 0.00 & 0.85 & 0.72 & 2427.73 & 835696.28 \\
& 11 in 2015 & 17144.95 & 879622.55 & 0.00 & 0.94 & 0.89 & 1246.52 & 1085362.00 \\
& 4 in 2015 & 8972.70 & 328139.30 & 0.00 & 0.96 & 0.92 & 555.38 & 435811.68 \\
& 10 in 2018 & 7161.79 & 179446.56 & 0.00 & 0.87 & 0.76 & 849.28 & 265388.08 \\
& 0 in 2015 & 6836.02 & 622702.52 & 0.00 & 0.69 & 0.48 & 1497.39 & 704734.72 \\
& 9 in 1994 & 6273.73 & 461446.27 & 0.00 & 0.71 & 0.50 & 1297.80 & 536731.04 \\
& 6 in 2018 & 5868.71 & 192494.97 & 0.00 & 0.92 & 0.86 & 503.29 & 262919.44 \\
& 7 in 2018 & 5833.80 & 372200.23 & 0.00 & 0.91 & 0.82 & 569.96 & 442205.88 \\
& 6 in 2005 & 5282.55 & 158014.62 & 0.00 & 0.77 & 0.59 & 913.11 & 221405.20 \\
& 28 in 2018 & 5171.32 & 305893.97 & 0.00 & 0.93 & 0.86 & 428.32 & 367949.84 \\
& 15 in 2018 & 5032.92 & 188544.60 & 0.00 & 0.93 & 0.87 & 401.49 & 248939.64 \\
& 2 in 2017 & 4913.84 & 248094.22 & 0.00 & 0.89 & 0.79 & 526.91 & 307060.24 \\
& 9 in 2018 & 4287.96 & 167592.31 & 0.00 & 0.91 & 0.83 & 404.87 & 219047.84 \\
& 12 in 2007 & 2259.43 & 333293.88 & 0.00 & 0.81 & 0.66 & 340.97 & 360407.00 \\
& 3 in 1995 & 964.03 & 272922.70 & 0.09 & 0.35 & 0.12 & 544.57 & 284491.00 \\
& 26 in 2012 & 487.62 & 242650.01 & 0.28 & 0.23 & 0.05 & 436.96 & 248501.48 \\
& 8 in 2009 & 470.18 & 566616.93 & 0.52 & 0.13 & 0.02 & 726.27 & 572259.04 \\
& 0 in 2013 & -4522.89 & 454055.07 & 0.17 & -0.28 & 0.08 & 3176.27 & 399780.44 \\
DE & 2 in 2018 & 21366.55 & 327039.44 & 0.00 & 0.98 & 0.96 & 928.87 & 583438.08 \\
& 8 in 2017 & 16311.05 & -17159.36 & 0.00 & 0.95 & 0.90 & 1128.43 & 178573.28 \\
& 34 in 2018 & 8504.39 & 155890.06 & 0.00 & 0.91 & 0.83 & 803.00 & 257942.68 \\
& 0 in 2018 & 8464.01 & 332669.71 & 0.00 & 0.97 & 0.95 & 407.17 & 434237.80 \\
& 8 in 2016 & 6789.83 & 205624.20 & 0.00 & 0.97 & 0.94 & 343.14 & 287102.20 \\
& 15 in 2015 & 5047.60 & 97312.68 & 0.00 & 0.97 & 0.94 & 275.66 & 157883.88 \\
& 24 in 2018 & 5027.07 & 58539.27 & 0.00 & 0.93 & 0.87 & 407.10 & 118864.16 \\
& 22 in 2017 & 3471.46 & 20050.75 & 0.00 & 0.94 & 0.88 & 265.79 & 61708.28 \\
& 9 in 1997 & 2690.08 & 85071.52 & 0.00 & 0.92 & 0.84 & 246.24 & 117352.48 \\
& 0 in 2010 & 2576.20 & 74740.47 & 0.00 & 0.83 & 0.69 & 361.66 & 105654.88 \\
& 0 in 2000 & 2520.76 & 376433.06 & 0.01 & 0.49 & 0.24 & 928.82 & 406682.20 \\
& 18 in 2018 & 2200.20 & 89329.56 & 0.00 & 0.89 & 0.80 & 232.43 & 115731.96 \\
& 4 in 2018 & 2150.07 & 196437.59 & 0.00 & 0.62 & 0.39 & 566.59 & 222238.48 \\
& 2 in 2012 & 1985.76 & 159928.37 & 0.00 & 0.84 & 0.70 & 269.83 & 183757.48 \\
& 29 in 2016 & 1954.78 & 60064.11 & 0.00 & 0.82 & 0.67 & 289.18 & 83521.44 \\
& 10 in 2018 & -12.21 & 118595.63 & 0.95 & -0.01 & 0.00 & 209.39 & 118449.08 \\
& 21 in 2011 & -611.70 & 76818.35 & 0.02 & -0.47 & 0.22 & 237.07 & 69478.00 \\
& 5 in 1996 & -976.76 & 62273.86 & 0.00 & -0.58 & 0.33 & 287.79 & 50552.76 \\
& 16 in 2000 & -2133.60 & 86460.10 & 0.00 & -0.89 & 0.80 & 225.41 & 60856.92 \\
& 19 in 1996 & -2923.61 & 59316.50 & 0.00 & -0.72 & 0.52 & 585.81 & 24233.20 \\
\bottomrule
\end{tabular}
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