Contribución del Big data en la dimensión Smart Environment

In Chapter Four I conduct a detailed case study of fragmentation and organi- zational formation among Republican militants in Northern Ireland, and I focus on two groups in particular: the Irish National Liberation Army (INLA) and the Real Irish Republican Army (RIRA). These two groups are chosen in particular because they share many similarities that ultimately help rule out a variety of alternative ex- planations for variation in group cohesion, collapse, and radicalization. For instance, both groups were formed by strong, charismatic leaders; both groups formed from the dominant organization of the time, and they bought sought to influence their parent groups before breaking away; both groups were partially inspired by parent groups adopting ceasefires; and finally, both groups formed under a pretext of in- creased cooperation and potentially compromise between militants and the British government.

There are other reasons to focus on Northern Ireland as well. Notably, it is a crucial case for studying militant fragmentation since the collapse and resultant creation of militants from existing groups was a critical component of the conflict decades-long conflict. There are few other cases where fragmentation has played such a central role, with John Morrison writing that “To suggest that splits have typified the development of Irish Republican militant groups is an understatement. . . These splits have not just shaped Irish Republicanism, they have led to some of the most significant and influential events in recent Irish history.”.2 Consequently, it is important that my

theory be able to explain developments in Northern Ireland if it is to be useful. Furthermore, Northern Ireland provides an excellent vantage point from which to study militant fragmentation due to the wealth of information available on the conflict. Indeed, there are innumerable books, articles, and reports, but there are also declassified documents both from the Irish and British governments as well as independent commissions and even documents from militants themselves (communi- cations, reports, newspapers, etc). In total, the extraordinary amount of information on Northern Ireland overshadows other cases and it provides ample evidence to un- derstand how group formation has played a role in the conflict’s evolution.

The goal of the case study is to test my theory and to understand if the proposed mechanisms are in fact at work, driving variation in rates of survival and radical- ization. The main difference between these two groups is that the RIRA formed factionally through a strategic disagreement with its parent group, the Provisional Irish Republican Army, whereas the INLA resulted from a multidimensional schism in then Official Irish Republican Army. Whereas the RIRA intended to resume a violent campaign to reunite Ireland by force, the INLA was motivated by both ideological and strategic concerns.

The case study presented in Chapter Four is supplemented by three months of research in Belfast, Dublin, and London. During this time I searched private and public archives in all three locations, looking for declassified government documents, internal group letters and communications, interviews, news reports, and academic publications that shed light on the inner working and decision-making process of these organizations. This research was vital, and it ultimately enhances my ability to understand the trajectory of both groups.

2

Building a Data Set of Organizational Fractures

Since data on militant fragmentation is not available in any existing database, I had to collect this information for the first time on my own. The resulting data set of organizational fractures is therefore the first of its kind and it should have many more uses beyond this specific project.

I first sought to understand the type and structure of a potential data set that would allow me to test my theory and provide satisfactory, credible results. There were three basic criteria that my data set would have to meet: first, it needs to contain data on both splinterand nonspliter organizations; second, it needs to be time-series; third, there need to be enough observations to support empirical estimation. The first point, that the data must contain both splinter and nonsplinter groups, is probably the most important. If I were to only examine splinter organizations then my results would be severely biased and the project would only tell me about the relative effects of different splintering conditions. Without a representative sample of all militant organizations, I would be unable to empirically evaluate how splinter groups compare to the average, nonsplinter organization, and this is crucial for testing my theory. Indeed, many argue that Robert Pape’s seminal study of suicide terrorism suffers from this exact flaw of selecting on the dependent variable.3 _{Ashworth, Meirowitz} and Ramsay write that “As a result, [Pape] cannot elucidate the causes of suicide terrorism; he cannot determine why some groups choose suicide tactics rather than other forms of resistance; and he cannot answer questions about the implications of various foreign policy choices on the incidence of suicide terrorism.4_{” Avoiding this} pitfall is therefore a primary aim of my data collection effort.

Next, it is also important to collect time series data for this project. This allows

3. Pape,Dying to Win.

4. Scott Ashworth et al., “Design, inference, and the strategic logic of suicide terrorism,”American Political Science Review 102, no. 02 (2008): 269–273.

me to construct a more fine-grained picture of organizational evolution and group fragmentation that is representative of the underlying causal narrative. Furthermore, time series data opens the door for a wider range of future projects that rely on the temporality and timing of events to explain, for example, group downfall and intergroup conflict. Many existing data sets on the characteristics of militant groups are cross-sectional, and they consequently limit researchers’ investigative ability.

And finally, producing robust and accurate empirical estimates requires sufficiently large samples. The precise number of observations will depend more on the number of parameters being estimated but in general, one cannot go wrong by having a larger sample. However, the desire for a larger sample has to be balanced with the feasibil- ity and time requirements of coding. This is especially a concern for this particular project since I am collecting this data for the first time, and identifying how each of these organizations form and when they subsequently splinter—if at all—is a dif- ficult, time consuming task. Nonetheless, I ultimately chose to randomly-select 300 observations (militant organizations) for the empirical analyses. This is feasible to code in a reasonable time frame while still providing sufficient observations for em- pirical estimation and enough geographic and temporal variation to make the results generalizable across space and time.

To construct this data set I began with the universe of groups identified by the Global Terrorism Database. I then removed numerous actors that do not concep- tually adhere to what I am interested in studying. First, I removed individual and unknown groups. Since my theory is specific to militant organizations, it should not apply to individual actors or unorganized groups of students or protesters. Second, I also remove organizations that never managed to kill a single individual. In other words, I subsetted the data to only those violent actors that managed to cause at least a single fatality throughout their lifetimes. I do this because I am not inter-

this project is concerned with the evolution of nonstate actors that intentionally use violence against civilians to further their political, social, or economic agenda. In ad- dition, groups that do kill civilians and those that never kill anyone are likely very different actors employing unique strategies of violence. Combining them into a single analysis and assuming a similar evolutionary trajectory would surely be problematic for the veracity of my findings.

Once these groups were removed the Global Terrorism Database, I merged the remaining information with data from the RAND-MIPT project on Terrorist Organi- zational Profiles (TOPs). This database details group ideology, goals, home country, maximum size, and other relevant information and characteristics. A major downfall fo this data set is its cross-sectional nature; in other words, it lacks time series infor- mation such that the number of group alliances is not calculated by year but rather it is the maximum number of alliances involving a particular organization. Despite this, TOPs is generally assumed to be the best and most comprehensive source of informa- tion on the subject of militant organizational characteristics. Following this merging process I was left with around 800 organizations that were subsequently included in both the GTD and the RAND-MIPT databases.

Each of these 800 organizations were randomly assigned a number from 0 to 800 using a random number generating process in STATA and the first 300 organiza- tions were chosen for this study. I chose 300—out of the full sample of 800—militant organizations for this project largely for the sake of feasibility. Coding these 300 or- ganizations was a time-consuming, difficult task that required many months of work. Coding the full sample of 800 would have been infeasible given that I also did field work in Northern Ireland. However, for future iterations of this project I hope to expand the coding to a much larger sample.

Once the random sample of organizations was created, I meticulously researched every organization to identify two main things: first, how the group formed. Did it

form by splintering away from another group? If so, I researched why it occurred, when it occurred, and who the original parent group was. Second, I then researched if this group itself had splintered at any point in its existence, and if so, into whom, when, and why. Thus, for each of these 300 organizations the data set details if the group itself was a splinter and whether or not that same group ever splintered throughout its existence. The variables accompanying these two events—why and when the splits occurred—are crucial to testing the hypotheses derived from my new theory of militant fragmentation.