Celebrating the birth (and death) of Magna Carta

Posted on June 15, 2015 by The Constitution Unit

On the 800th anniversary of Magna Carta, James Melton reflects on the strength and influence of the legal document, but also its weaknesses and negative aspects.

This article is based on a new volume co-edited by Robert Hazell and James Melton Magna Carta and Its Modern Legacy.

Today Magna Carta (1215) celebrates its 800^th birthday. We celebrate this historic event for two reasons. First, the Great Charter is one of the oldest in force legal documents in the world, as four lines from the original charter are still on the statute books in the United Kingdom. Second, and more importantly, Magna Carta has become a symbol of limited government that is recognised all over the world. However, when celebrating the 800^th anniversary of Magna Carta’s birth, one should not forget about the 800^th anniversary of its death on 24 August 2015. Magna Carta (2015) died when it was annulled by Pope Innocent III, just two months and nine days after it was sealed. The Great Charter was subsequently amended and reissued in 1216, 1217, 1225 and 1297, but its early death serves as a reminder that, in 1215, Magna Carta was a failure. It was completely ignored by King John and, ultimately, led England into the very civil war which it was meant to prevent.

In our new volume, Magna Carta and Its Modern Legacy, Robert Hazell and I have put together a collection of essays that both commemorate Magna Carta’s 800 year history and provide a balanced assessment of the Great Charter’s legacy. The volume is divided into three sections: 1) Magna Carta’s influence in the UK, 2) its influence abroad and 3) 21^st century reflection on Magna Carta. While the scholars who have contributed to our volume all recognize the symbolic importance of Magna Carta, they all also realise that many of the claims made about Magna Carta are grossly exaggerated. As a result, the account of Magna Carta told by their chapters is more realistic than the account told by many commentators. Instead of unbridled enthusiasm for the Great Charter, our contributors recognise that its influence has not been wholly positive.

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Codification of the UK Constitution is not essential

Posted on March 19, 2015 by The Constitution Unit

The Constitution Unit is pleased to announce the launch of a new report To Codify or Not to Codify: Lessons from Consolidating the United Kingdom’s Constitutional Statutes. James Melton, the report’s lead author, offers an overview of the report, which reflects on some lessons learned about the UK Constitution while consolidating the texts of 18 constitutionally relevant statutes. The main conclusion is that further codification is not essential.

I have devoted much of my academic career to studying national constitutional texts. As one of the principle investigators on the Comparative Constitutions Project, a project dedicated to cataloguing the contents of national constitutional texts, I have often found myself defending the power and importance of formal constitutional entrenchment against critics who view constitutional texts as ‘mere parchment barriers’. In doing so, I am, at least implicitly, arguing that codification of a country’s constitutional order is beneficial

However, if codification is so beneficial then the UK is a real puzzle. It is a country whose constitution is famously ‘unwritten’ but that has evolved into a constitutional monarchy that performs well on cross-national measures of democratic performance, economic performance, governance and the rule of law. So, when I moved to the United Kingdom, nearly three years ago, I thought it would be an excellent opportunity to study the UK’s constitution and to understand the problems created by its lack of codification. After all, there must be some benefit from adopting a codified constitution; otherwise, almost every other country in the world would not have adopted one. So the UK, and the handful of other countries without a codified constitution, must be missing out on something.

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New Data Available from the Comparative Constitutions Project

Posted on April 23, 2014 by j.melton

Last week the Comparative Constitutions Project (CCP) released two new data sets. These new data are available for download here.

The first data set is an update to the CCP’s Chronology of Constitutional Events. In addition to making a number of minor changes to previously released chronology data, we added constitutional events that occurred from 2007 to 2013. According to our records, there have been 25 new constitutions and 4 interim constitutions written from 2007 through 2013 and 172 constitutional amendments.

The second data set is a major expansion of the CCP’s data on the Characteristics of National Constitutions. The original release of these data (version 1.0) included only the contents of each constitution in force in 2006. The expanded data (version 2.0) contains all of the “cleaned” data available from the project. This includes data from 524 constitutional systems and nearly 1,500 constitutional events, which equates to more than 8,000 country-years.

The figure below illustrates the relationship between the CCP’s characteristics data and its chronology. The dashed line indicates the number of independent states in each year from 1789 to 2013. The solid line indicates the number of constitutions in force in independent states in each of those years. The shaded area represents the country-years for which the CCP has released data on the characteristics of national constitutions. Overall, the characteristics data are available for 52% of country-years for which there was a constitution in force. As illustrated in the figure, this percentage varies quite a bit over time, ranging from only having data available for about 40% of countries circa World War II to data available for about 85% of countries for much of the last decade.

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Comparing the 1971, 2012, and 2013 Egyptian Constitutions

Posted on December 12, 2013 by j.melton

The Comparative Constitutions Project has developed a new tool that allows one to compare the 1971, 2012, and 2013 Egyptian constitutions: http://comparativeconstitutionsproject.org/comparing-the-egyptian-constitution/. We have assigned the topics from the Constitute site to the texts of those three constitutions, which allows one to select a topic and see how each of the three constitutions addresses it. If you are interested in how much (or little) the Egyptian constitution has changed as a result of the Arab Spring, then I encourage you to take a look.

(Please send me an e-mail or make a comment if you notice any errors in our assignment of topics.)

Directly Querying the Constitute Data

Posted on December 11, 2013 by j.melton

Thank you to all who attended my seminar today. As promised, I am going to provide the code that I used to query the data underlying the Constitute site.

To start, you will need to know how to write a SPARQL query. There are good resources online to teach you how to write such queries (see here or here). Once you know a bit about writing SPARQL queries, you can test out your skills on the data underlying the Constitute site. Just copy and paste your queries here. To get you started, here are the two queries that I used in my seminar:

Query 1:

PREFIX ontology:<http://tata.csres.utexas.edu:8080/constitute/ontology/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
SELECT ?const ?country ?year
WHERE {
?const ontology:isConstitutionOf ?country .
?const ontology:yearEnacted ?year .
}

Query 2:

PREFIX ontology:<http://tata.csres.utexas.edu:8080/constitute/ontology/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
SELECT ?const ?country ?year ?region ?sectionType ?sectionText ?childType ?childText
WHERE {
?const ontology:isConstitutionOf ?country .
?const ontology:yearEnacted ?year .
?section ontology:isSectionOf ?const .
?country ontology:isInGroup ?region .
?section ontology:hasTopic ontology:env .
?section ontology:rowType ?sectionType .
OPTIONAL {?section ontology:text ?sectionText}
OPTIONAL {?childSection ontology:parent ?section . ?childSection ontology:text ?childText}
OPTIONAL {?childSection ontology:parent ?section . ?childSection ontology:rowType ?childType}
}

Notice the “topic” line in the second query (?section ontology:hasTopic ontology:env .). The env part of that line is the tag that we use to indicate provisions that deal with “Protection of environment”. You can explore the list of topics included on Constitute and their associated tags here.

The next step is to apply your querying knowledge using the SPARQL package in R. I will demonstrate how this is done by walking you through the creation of the Word Cloud that I discussed during my seminar (the code for the histogram is easier to understand and is below). First, query the SPARQL endpoint using R:

#Opens the Relevant Libraries
library(SPARQL)
#Defines URL for Endpoint
endpoint <- "http://tata.csres.utexas.edu:8080/openrdf-sesame/repositories/test"
#Defines the Query
query <- "PREFIX ontology:<http://tata.csres.utexas.edu:8080/constitute/ontology/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
SELECT ?const ?country ?year ?region ?sectionType ?sectionText ?childType ?childText
WHERE {
?const ontology:isConstitutionOf ?country .
?const ontology:yearEnacted ?year .
?section ontology:isSectionOf ?const .
?country ontology:isInGroup ?region .
?section ontology:hasTopic ontology:env .
?section ontology:rowType ?sectionType .
OPTIONAL {?section ontology:text ?sectionText}
OPTIONAL {?childSection ontology:parent ?section . ?childSection ontology:text ?childText}
OPTIONAL {?childSection ontology:parent ?section . ?childSection ontology:rowType ?childType}
}"

#Queries the endpoint
sparql <- SPARQL(endpoint,query,ns=c('ontology','<http://tata.csres.utexas.edu:8080/constitute/ontology/>','const','<http://tata.csres.utexas.edu:8080/constitute/>'))

You now have a data table with the relevant textual data available to you in R under sparql$results. The second step is to organize that data into a corpus using the text mining package (tm, for short). Ultimately, I am only interested in rows in the data table that have text (i.e. the sectoinText and childText columns are not empty) and that are from constitutions written in the Americas or Africa, so I will filter the data along these lines in this step of the process. Here is the code:

#Opens the Relevant Libraries
library(tm)
library(SnowballC)

#Filters Out Correct Regions
data.Africa <- subset(sparql$results,sparql$results$region=="const:ontology/Africa")
data.Americas <- subset(sparql$results,sparql$results$region=="const:ontology/Americas")

#Extracts Section Text from Results and Removes Missing Values
sText.Africa <- subset(data.Africa,data.Africa$sectionText!='NA')
sText.Africa <- subset(sText.Africa$sectionText,sText.Africa$sectionType=="const:ontology/body")
sText.Americas <- subset(data.Americas,data.Americas$sectionText!='NA')
sText.Americas <- subset(sText.Americas$sectionText,sText.Americas$sectionType=="const:ontology/body")

#Extracts Child Section Text from Results and Removes Missing Values
cText.Africa <- subset(data.Africa,data.Africa$childText!='NA')
cText.Africa <- subset(cText.Africa$childText,cText.Africa$childType=="const:ontology/body")
cText.Americas <- subset(data.Americas,data.Americas$childText!='NA')
cText.Americas <- subset(cText.Americas$childText,cText.Americas$childType=="const:ontology/body")

#Appends Parent and Child Text Together
Text.Africa <- data.frame(c(sText.Africa,cText.Africa))
Text.Americas <- data.frame(c(sText.Americas,cText.Americas))

#Converts Data Frames to Corpora
corpus.Africa <- Corpus(VectorSource(Text.Africa))
corpus.Americas <- Corpus(VectorSource(Text.Americas))

Now that I have organized the relevant text into corpora, I need to clean those corpora by removing stop words (e.g. a, an and the), punctuation and numbers and stemming words. This is standard practice before analyzing text to prevent “the” from being the largest word in my word cloud and to make sure that “right” and “rights” are not counted separately. The tm package has all the tools to perform this cleaning. Here is the code:

#Makes All Characters Lower-Case
corpus.Africa <- tm_map(corpus.Africa,tolower)
corpus.Americas <- tm_map(corpus.Americas,tolower)

#Removes Punctuation
corpus.Africa <- tm_map(corpus.Africa,removePunctuation)
corpus.Americas <- tm_map(corpus.Americas,removePunctuation)

#Removes Numbers
corpus.Africa <- tm_map(corpus.Africa,removeNumbers)
corpus.Americas <- tm_map(corpus.Americas,removeNumbers)

#Removes Stopwords
corpus.Africa <- tm_map(corpus.Africa,removeWords,stopwords('english'))
corpus.Americas <- tm_map(corpus.Americas,removeWords,stopwords('english'))

#Stems Words
dict.corpus.Africa <- corpus.Africa
corpus.Africa <- tm_map(corpus.Africa,stemDocument)
corpus.Africa <- tm_map(corpus.Africa,stemCompletion,dictionary=dict.corpus.Africa)
dict.corpus.Americas <- corpus.Americas
corpus.Americas <- tm_map(corpus.Americas,stemDocument)
corpus.Americas <- tm_map(corpus.Americas,stemCompletion,dictionary=dict.corpus.Americas)

The last step is to analyze the cleaned text. I created a simple word cloud, but you could perform even more sophisticated text analysis techniques to the textual data on Constitute. I used the wordcloud package to accomplish this task. Here is the code I used to create the word clouds for my presentation:

#Opens the Relevant Libraries
library(wordcloud)
library(RColorBrewer)
library(lattice)

#Creates a PNG Document for Saving
png(file="WC_env.png", height = 7.5, width = 14, units = "in", res=600, antialias = "cleartype")

#Sets Layout
layout(matrix(c(1:2), byrow = TRUE, ncol = 2), widths = c(1,1), heights = c(1,1), respect = TRUE)

#Sets Overall Options
par(oma = c(0,0,5,0))

#Selects Colors
pal <- brewer.pal(8,"Greys")
pal <- pal[-(1:3)]

#Word Cloud for the Americas
wordcloud(corpus.Americas,scale=c(3,0.4),max.words=Inf,random.order=FALSE,rot.per=0.20,colors=pal)

#Creates Title for Americas Word Cloud
mtext("Americas",side=3,cex=1.25,line=4)

#Word Cloud for Africa
wordcloud(corpus.Africa,scale=c(3,0.4),max.words=Inf,random.order=FALSE,rot.per=0.20,colors=pal)

#Creates Title for African Word Cloud
mtext("Africa",side=3,cex=1.25,line=4)

#Creates an Overall Title for the Figure
mtext("Constitutional Provisions on the Environment",outer=TRUE,cex=2,font=2,line=1.5)

#Closes the Plot
dev.off()

Note that the plotting commands above are complicated by the fact that I wanted to combine two word clouds into the same image. Had I only wanted to create a single word cloud, say for Africa, and did not care much about the colors of the plot, the following commands would have sufficed:

#Opens the Relevant Libraries
library(wordcloud)

#Word Cloud for Africa
wordcloud(corpus.Africa,scale=c(3,0.4),max.words=Inf,random.order=FALSE,rot.per=0.20,colors=pal)

Anyway, here is the resulting word cloud:

With the commands above, you should be able to replicate the word clouds from my seminar. In addition, minor modifications to the commands above will allow you to describe the constitutional provisions on different topics or to compare the way that different regions address certain topics. One could even perform more advanced analyses of these texts with the SPARQL queries outlined above.

CODE FOR HISTOGRAM

#Opens the Relevant Libraries
library(SPARQL)
library(RColorBrewer)

#Defines URL for Endpoint
endpoint <- "http://tata.csres.utexas.edu:8080/openrdf-sesame/repositories/test"

#Defines the Query
query <- "PREFIX ontology:<http://tata.csres.utexas.edu:8080/constitute/ontology/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
SELECT ?const ?country ?year
WHERE {
?const ontology:isConstitutionOf ?country .
?const ontology:yearEnacted ?year .
}"

#Makes the Query
sparql <- SPARQL(endpoint,query,ns=c('ontology','<http://tata.csres.utexas.edu:8080/constitute/ontology/>','const','<http://tata.csres.utexas.edu:8080/constitute/>'))

#Subsets Data
data.year <- data.frame(subset(sparql$results,select=c("const","year")))

#Drops Duplicate Observations
data.year <- unique(data.year)

#Makes Year Numeric
year <- as.numeric(data.year$year)

#Creates PNG Document for Saving
png(file="Histogram_Year.png")

#Selects Colors
pal <- brewer.pal(3,"Greys")
pal <- pal[-(1:2)]

#Histogram Command
hist(year, breaks=21, col = pal, border = pal, xlab = "Promulgation Year", ylab = "Number of Constitutions", ylim = c(0,60), xlim = c(1790,2010), main = "Constitutions on Constitute")

#Closes the Plot
dev.off()

And here it is: