Galtung’s AJUS System

Galtung (1967) introduced the AJUS system as a way to classify distributions according to shape. This is a means to reduce complexity. The underlying idea is to classify distributions by ignoring small differences that are not important. The system was originally developed for eye-balling, but having it done by a computer makes the classification more systematic.

All distributions are classified as being one of AJUS, and I have added a new type “F” to complement the ones identified by Galtung.

  • A: unimodal distribution, peak in the middle
  • J: unimodal, peak at either end
  • U: bimodal, peak at both ends
  • S: bimodal or multi-modal, multiple peaks
  • F: flat, no peak; this type is new

The skew is given as -1 for a negative skew, 0 for absence of skew, or +1 for a positive skew. The skew is important for J-type distributions: it distinguishes monotonous increase from monotonous decrease.

I have implemented the AJUS system in my R package agrmt. By setting the tolerance, we can determine what size of differences we consider small enough to be ignored. The default tolerance is 0.1, equivalent to 10% if using 0 to 1. AJUS implemented in R sets a systematic threshold, something we do not do when eye-balling differences.

The tolerance parameter is not a trivial choice, but a test is included in the R package to directly test sensitivity to the tolerance parameter (ajusCheck).

Here are some examples (using the experimental ajusPlot function and tolerance = 10):
plot_
Differences smaller than the tolerance set (10) are ignored.

Reference: Galtung, J. 1969. Theory and Methods of Social Research. Oslo: Universitetsforlaget.

Do Civil Society Organizations See Immigration Positively?

Civil society organizations (CSO) are important political actors in the debate on immigration. As part of the SOM project we examined the politicization of immigration in seven Western European countries, 1995 to 2009. Civil society organizations are responsible for between 11 and 28 per cent of claims in the news.

With the exception of the UK, most of the claims by civil society organizations are positive: Between around 70 and 80 per cent of claims by civil society organizations on immigration are positive.

Edited on 1 Feb 2013: Removed some incorrect numbers; the patterns is generally observed.

Installing MS Office 2003 on GNU/Linux

I own a copy of Microsoft Office 2003, and here’s how I managed to install it under GNU/Linux (Debian) using Wine. Most versions of office 2003 install without problems, so this post does not apply. However, there are some versions (mine included), where the installer fails (i.e. it quits with an error).

The solution was to install an old version of Wine, namely Wine 1.2, from the Wine archives. If you have Wine installed, remove it (apt-get remove…) and install version 1.2 manually (dpkg). You can then install Office 2003 as expected.

Unfortunately using Wine 1.2, the activation of the software does not work. The solution here is to install the latest version of Wine in the Debian repository (apt-get install wine). Once Wine 1.4.1 is running, activating the software is no problem, and Office 2003 works.

(Yes, in an ideal world I would not need Microsoft Office, but LibreOffice cannot [yet] compete when it comes collaboratively work on Word files. It routinely messes up the layout, and regularly corrupts Word files when recording changes.)

Wordscores in R

A while ago I wrote a step by step guide for using Wordscores in R. I did this as part of the FP7 project SOM, but ended up doing the analyses myself. I have recently posted some code on running Wordscores in R:

# #################################
# WORDSCORES AND WORDFISH ANALYSIS
# #################################

# setup
library(austin)

############################
# GETTING DOCUMENTS IN
############################
a <- wfm("SHORT.1995-2011.csv")
a[0,] # check the party order (header only)

############################
# A. WORDFISH
############################
wordfish(a, dir=c(23, 20), control=list(tol=1e-06, sigma=3, startparams=NULL), verbose=FALSE)
# identification strategy:
# GPS 2003 and SVP 2003
# these are the extremes in the expert survey (moving average or alternative count)
# also they are nicely the Benoit & Laver texts, for which we have some confidence

############################
# B. WORDSCORES
############################

# SET REFERENCES
ref <- c(10,11,15,20,23) # reference texts
vir <- 1:24 # SPS 2011 (short) is empty, thus not included
vir <- vir[-ref] # everything minus the reference texts

r <- getdocs (a, ref)
ws <- classic.wordscores(r, scores=c(5.971929825,1.252631579,4.665789474,9.206140351,0.935087719))
summary(ws)

# PREDICT
v <- getdocs (a, vir)
predict(ws,newdata=v)

I use Will Lowe’s JFreq to get the word frequencies.

POS: Are Federal Systems More Open?

In the literature on political opportunity structures (POS), it is commonly held that federalism means more access points. More access points mean more opportunities.

It does not suffice, however, only to look at the number of access points. Leaving aside the fact that some movements may not be acceptable to potential access points, more potential access points mean a greater probability of findings an ally. However, more potential allies also means that the influence of a single ally is more restricted. Therefore, it is not at all clear that the opportunity (as in getting influence on policy-making, for example) is greater in federalist systems.

In federal systems, the entry might be easier, but the impact might be limited. If an organization gets access in a more centralized system, the impact is at once more significant. For this reason, I suggest that we should expect no significant differences overall.

Perhaps there is another mechanism that favours federalism, perhaps it is the inclusive political culture? In this case we should focus on the relevant variable. Perhaps it is acceptable to concentrate on the degree of federalism as a measure of political culture (inclusiveness), but the argument would need to be a bit different.