From the Mob to Terrorists: How RICO has Affected Prosecutions


This continues our series of student reflections and analysis authored by our research team.


The  Racketeer Influenced and Corrupt Organizations Act, commonly known as RICO, has gone far beyond the bounds of its initial intention. Enacted by congress in 1970, RICO was passed “with the declared purpose of seeking to eradicate organized crime in the United States” (Department of Justice n.d.). Under RICO, the United States Government was able to prosecute crimes committed by criminal organizations as a whole. This meant that when a case was brought against an individual, the organization that they were a part of, or in any way contributing to, would also be brought to justice. Not only can RICO be applied to a criminal act, and prosecuted at a federal level, but RICO also has civil implications. In a civil suit, RICO is a powerful weapon which allows individual citizens to bring others to court regarding criminal enterprises. However, many of the same processes still apply.

Despite having vague wording, and a broad interpretation, a RICO indictment must meet a set of criteria.The RICO act explicitly outlines 35 offenses which constitute racketeering. The most intense being “gambling, murder, kidnapping, arson, drug dealing, and bribery” (“Racketeer Influenced and Corrupt Organizations (RICO) Law” n.d.).  However, there are two offenses which are included under RICO which have the farthest reach, and allow for vast interpretation: mail and wire fraud. In order to execute a RICO indictment however, there must be proof, beyond a reasonable doubt that an “enterprise,” or criminal organization, exists. The economic reason that RICO is a federal offense, is there must be proof that the actions of the organization affected interstate commerce. Then there must be proof that the defendant was associated with the organization in question, and “engaged in a pattern of racketeering activity” (Department of Justice n.d.). And finally, that the indictment provides at least two instances of racketeering which the defendant participated in, or assisted the organization with. Both instances included in the indictment must have occurred within a ten year period. These prior offenses, known as “predicate acts” must prove the potential for future criminal activity, and/or involvement in the enterprise (Department of Justice n.d.).

The interpretation of the ‘enterprise’ is also expansive, and allows for a broad understanding by the courts, as was the initial intention of congress. Under the RICO act, “enterprise is defined as including any individual, partnership, corporation, association, or other legal entity, and any union or group of individuals associated in fact although not a legal entity” (Department of Justice n.d.). In layman’s terms, any individual, or group of individuals, de jure or de facto, which participate in acts of racketeering in order to further the goals of their organization, constitute an enterprise.

Given the inherent liberal interpretation of RICO, the US government has been able to turn the focus from organized crime such as the old Irish Mob, and begin to focus on more modern forms of organized crime. This doesn’t just mean gangs and drug cartels, however. RICO has expanded its already broad interpretation to also encompass terrorist organizations with operations in the United States. RICO has become an important part of the war on terror, despite appearing minimally in the data set thus far. Because of RICOs financial implications, prosecutors are able to apply RICO to people who attempt to provide material support to terrorist organizations. If a group is designated a terrorist group, providing material support has become a prosecutable offense. Organizations can also be prosecuted for act of terrorism “for events that occurred prior to official designations of the terrorist organizations involved,” giving RICO a more powerful role in law enforcement (Perquel 2015).

-Emily Lightman


References

Department of Justice. N.d. “RICO CHARGES.” U.S. Department of Justice. Accessed December 2, 2018. https://www.justice.gov/jm/criminal-resource-manual-109-rico-charges.

Perquel, Anne-Laure. 2015. “The Use of RICO in the War against Terror.” April 2015, 1-25.   http://www.academia.edu/12545128/The_use_of_RICO_in_the_war_against_terror.

“Racketeer Influenced and Corrupt Organizations (RICO) Law.” n.d. Justicia Law. Accessed December 2, 2018. https://www.justia.com/criminal/docs/rico/.

Group Affiliation and Ideologies Through Heat Maps


This continues our series of student reflections and analysis authored by our research team.


When tPP was still in its coding stage, I enjoyed perusing through documents to find the codable variables within our dataset. I was consistently interested in where defendants fell in terms of ideology and group affiliation. I would often notice similarities between certain groups. I had questions about what may have an influence on the patterns I was seeing. When deciding on a topic for my analysis, I knew that I wanted to look into group affiliation on a deeper level.

A defendant’s affiliation generally has a big impact on a multitude of other variables like othered status, location of attack, foreign affiliation and more. The challenging part of focusing on group affiliation was deciding on what other variables to investigate in combination with it. My first attempt at an analysis of the dataset looked at the effect of othered status and foreign affiliation on group affiliation versus no affiliation. I was able to use the numbers from the dataset to produce a large amount of facts and figures, but it quickly proved to be too many variables and far too many patterns to analyze. I did not find relevant patterns in the areas I was expecting, either. I was interested in far too many variables to produce an intuitive paper that explored group affiliation in the manner it deserves.

I started to organize the entire dataset by utilizing pivot tables within Excel. Pivot tables are tables of statistics used to summarize another, larger data table such as tPP’s. With this feature, I was able to easily insert or remove any variable from the dataset in order to see what kind of findings it would produce. I stuck with using group affiliation as a base and Excel provided me with massive tables, grouping defendants into rows. It showed me precisely how many of them were involved with each group. When inserting a variable like foreign affiliation, I was able to easily see which defendants had a connection to a foreign country. The titles shown on the table were no, yes and unknown which are the three options for foreign affiliation coding. Underneath each title was a list of group affiliations that fit into the category. Each group affiliation row showed the number of defendants that applied to one of the categories. While using each of the 1,194 defendants data produced an overwhelming amount of numbers, I was able to identify a handful of clear and relevant patterns. For example, 91.6% of the defendants who are affiliated with Al Qaeda are foreign affiliated (132 people). When looking at the 348 defendants who have no group affiliation, the PivotTable clearly shows an overwhelming majority (330 of 348) have no foreign affiliation. This method of organizing the data’s numbers was very helpful in deciding what specific groups and variables would produce information worth investigating further.

 

The sample of tables below show a part of a pivot table that looks at group affiliation in relation to foreign affiliation as described above.

While I enjoyed looking into each of the variables, I struggled to write an analysis on group affiliation in relation to just one of the other variables. I ended up using a lot of the relevant findings in my writing, but it did not come together well; it was too broad. I eventually decided to analyze the data in a way that I had not considered before. I chose to use heat mapping to represent where individual attacks had occurred. Rather than putting all of the data into the system, I chose to sort by group affiliation. Through this method, I have begun to reveal findings through analyzing group affiliation and ideology in relation to geographic location which has already proven to show relevant patterns of attack.

– Jessica Enhelder

Reexamining the Codebook


This continues our series of student reflections and analysis authored by our research team.


In class the past few weeks, we have decided to reassess and rework our team’s Codebook. Up until now, the Codebook has been a relatively straightforward manual that we all follow while coding each case. Personally, my partner and I leave the Codebook open in another tab while we research a defendant – referring to it when we have specific questions about what codes make up a certain variable or how exactly a variable is defined.

For the most part, we have the Codebook memorized. Since the majority of us joined the team as the Codebook was being created and fine-tuned, we understand what each variable is looking to assess. This is not to say the Project has not come across some interpretation issues as we have worked through coding. A few weeks ago, we had a lengthy discussion about how to code cases in which the defendant was a minor at the time of the crime. Since portions of these cases, including ages of the defendant are often sealed from the public, it can be difficult to know what the actual age was. Some members of the team had been coding this variable as unknown, while others had been using 17 as a fill-in number, as our Codebook had not specified what to do in this situation. We talked in-class and decided to use 17 as our age for all minors, unless the actual age was known, in which case we would use that one.

Differing interpretations of the Codebook occur regularly, and we do our best to address them and amend the document to make each variable description even more clear. However, next semester, we are adding several new members to the Prosecution Project. These new additions will have the guidance of senior members of the team as they learn how to code, but they will still be heavily reliant on the exact wording of the Codebook to understand each variable and its codes. Because of this, the team has spent the last two classes going through each individual variable in the Codebook in extreme detail. Our goal is to phrase the Codebook so that there can be almost no room for misinterpretation. We begin by phrasing each variable as a question.

This sounds relatively straightforward and self-explanatory, but as the team and I can assure you, it is not. We have dedicated at least ten minutes to discussing how to phrase the questions for each variable, and some variables have required conversations lasting over an hour. One particularly challenging and divisive conversation occurred surrounding the variable of “previous similar crime”. We struggled with how to conceptualize what constituted a similar crime, and by the end of the discussion, we had probably run through thirty different variations of the question. Our first suggestion was, “Has the defendant been charged with a previous similar crime?” and by the end, we decided on, “Has the defendant been charged or convicted of a previous crime motivated by the same belief system?” We felt that this phrasing was the best way to encompass what we wanted to assess with this variable.

Eventually, we will have worked through this process for every variable in our Codebook. Ideally, we will have a user-friendly manual for next semester’s additions, but we are ready and willing to further adjust our Codebook as new issues arise.

– Zoe Belford

A Codebook 2.0?


This continues our series of student reflections and analysis authored by our research team.


 As the semester is winding down, here is an update on the current status and goals of tPP! Over the past two months, everyone has worked to construct mini-analyses papers on a chosen topic surrounding our database. Some members worked in pairs while others worked individually to assess trends that may be appearing within the database. The papers addressed several different factors we have coded for such as gender prevalence in terrorism, foreign affiliation and fatality, military/veteran status and its role in attacks, location prevalence, etc. We plan to start out next semester by presenting our papers and findings to the entire team as a reminder of all the great work we have achieved so far!

Speaking of the team, we are also excited next semester to welcome some new recruits! We have spent time recently reviewing our meeting agenda and drafting not a new, but a more explicit, and more concise codebook that will be extremely beneficial when catching the new members up to speed! Kicking off the new year, we will ultimately finish adding and coding cases, so we can continue to draw final analyses of patterns of taxonomy within our dataset. As we begin to move toward the final stages of our project, we aim to draft more literature, advertising the information presented in our data, and work to present our findings to outsiders at relative conferences!

As a member of tPP from the start, and a soon graduating senior, this experience has been eye-opening as much as it has been informative. Working with Dr. Loadenthal and the rest of the team has caused an interest in continuing research around the justice system and helped prepare me to keep to higher education in ways I would have never received without them. With tPP being one of the largest datasets of its kind, it has offered so many undergraduate students the chance to participate in research that while tasking, has been extremely rewarding. The project is mostly student-led has allowed us to learn and improve our skills in leadership, collaboration, research, statistical analyses, technical writing, and so many more. Many members have been on the team since the start and found their niche through this project and enjoy the chance to collaborate on a regular basis to adopt roles and goals as needed within our own mini projects and the larger project as a whole. This spring will be an exciting time for everyone as we move to our final stages but the time cannot come soon enough for our eager current and new members.

– Tia Turner

tPP in the news again! This time a short video interview with project Director

Following coverage of tPP by our university news, tPP Director Dr. Michael Loadenthal sat down with Sinclair Broadcast Group for a 30-minute interview about the project, the state of political violence in the US, and the challenges of researching these matters. From this interview we are happy to share a short segment produced by Sinclair below.

We were also happy to be mentioned in Miami University’s College of Arts and Science Alumni Update for November 2018 which you can see below:

Excluded Cases and Why They Remain Important


This continues our series of student reflections and analysis authored by our research team.


The tPP data set[1] has an extensive process of selecting cases that fit the criteria for the database.  This process is called the decision tree and has been described in other blog posts.  While the data set currently has almost 1,202 coded cases, there are some cases that did not meet the qualifications of the decision tree at some point in time and ended up becoming excluded.  These cases appear to be ones that would be relevant to the set but they fall short of particular qualifications.  When a case is excluded it is placed into a document of excluded cases where it is briefly explained and then its exclusion is subsequently explained as well.  Some may wonder why we bother to record cases that are not matches for us, well, many these excluded cases can reveal information about the tPP data set itself.

Some excluded cases are straightforward to explain, such as the case of William Rodgers.[2]  William Rodgers was an environmental activist and major leader of an act of arson at a Vail Ski Resort in Colorado.  He is excluded from the data set because he committed suicide in jail shortly after he was arrested.  Since he was not able to be charged and prosecuted, he is excluded from the tPP data set.

Other cases in the excluded cases file deal with more complicated issues such as intent.  Intent becomes crucial in determining whether or not to omit particular cases from the dataset.  Does the individual committing the act of terrorism or political violence truly possess a political motive?  Are their crimes attempting to further a particular terrorist organization or movement?

The tPP dataset contains many variables that are coded with very precise language to ensure that intent is the primary focus of the coding.  Some of these variables include ‘people versus property’ or ‘ideological affiliation’.  People versus property outright asks “Did this crime intend to target human beings, material property, both or neither?”[3]  This seeks to determine the intent of whom the crime was trying to cause harm towards.  Ideological affiliation is defined by the codebook as “What belief system, if any, motivated the defendant to commit the crime?”[4] This variable also focuses on what the core value system of the individual is and this can affect the intention of their crime.  If one throws a brick threw a McDonald’s window out of anger it would not be considered terrorism.  However, if they had an ideology that opposed consumption of animals and they committed the same crime, the same act could be considered an act of political violence, and likely termed by the government as ‘eco-terrorism’.

These variables show the emphasis that the data set places on intent.  The excluded cases are a variety of examples where the acts may be heinous, or may present rhetoric that is similar to what one may consider to be terrorism, however, this specific data set takes into careful consideration intent, and every case must fully pass through the decision tree before it qualifies to be coded into the data set.  These excluded cases are still valuable, as they show the value this tPP data set places on intent.

– Hannah Hendricks


References

[1]Loadenthal, Michael, Zoe Belford, Izzy Bielamowicz, Jacob Bishop, Athena Chapekis, Morgan Demboski, Bridget Dickens, Lauren Donahoe, Alexandria Doty, Megan Drown, Jessica Enhelder, Angela Famera, Kayla Groneck, Nikki Gundimeda, Hannah Hendricks, Isabella Jackson, Taylor Maddox, Sarah Moore, Katie Reilly, Elizabeth Springer, Michael Thompson, Tia Turner, Brenda Uriona, Brendan Newman, Jenn Peters, Rachel Faraci, Maggie McCutcheon, and Megan Zimmerer, 2018. “The Prosecution Project (tPP) October 2018” Miami University Sociology Department. https://tpp.lib.miamioh.edu. Loadenthal 2018. “The Prosecution Project (Decision Tree)”

[2] (Loadenthal et. al, 2018)

[3] (Loadenthal et. al, 2018)

[4] (Loadenthal et. al, 2018)

 

Gender Disparity in Political Crimes: Revealing tPP’s Strengths and Limitations


This continues our series of student reflections and analysis authored by our research team.


Taking a step back from coding further cases for the dataset, tPP’s researchers wanted to take the time to answer some of the questions which had arisen around the trends we identified over months researching and coding prosecutions of political crimes. For some of us, our questions involved whether we could possibly confirm or correct- now with the compiled statistical evidence- the hypotheses and apparent observations we came to make along the way.

There are few trends and correlations between the variables in tPP’s database as blatant as the gender disparity across defendants prosecuted for acts of political violence and other crimes. To those coding cases for tPP, it had always been apparent that there were far more male than female defendants, however with a sample set of 1,193 coded cases in the temporarily finalized database, I was able to run the numbers and confirm that observation.

In attempting to find why only 7.38% of the defendants in tPP’s database were female, I pursued two possible explanations. The first is the explanation which tPP’s data is capable of and in fact designed to answer: could there be certain variables which are “prerequisites” to women committing political violence, or conditions which are vital factors for women to commit such acts, but not for men to do so? If such a condition exists, strong positive correlations between cases having both female defendants and being coded positively for the predictor variable- stronger correlations than exist between male defendants and the predictor variable- ought to exist.

My further research into this possibility examines the correlations between women’s engagement in political crimes and a number of variables of association  tPP codes for, including the co-defendant, group affiliation, and ideological affiliation variables. These three variables are descriptive of cooperative as opposed to independent engagement in political crimes, with the condition of cooperation seeming to be the common “prerequisite” for female engagement. This analysis revealed for example that only 15.91% of female defendants as opposed to 32.85% of male defendants did not have any co-defendants. Similarly, only 6.82% of the female defendants had neither a co-defendant nor a group affiliation, making their actions truly independent, as opposed to 14.57% of men in the database. Thus, it does seem to support the hypothesis that women more often than men participate in cooperative political violence or crimes, making them less likely to act independently or as a “lone wolf”, and therefore making their engagement in such acts less common.

However, it is important to remember that there is a second, much researched explanation- that women are charged and prosecuted less than men for these sorts of crimes, regardless of the rate at which they engage in them- that tPP’s data is not designed to answer. This is due to the very nature of tPP’s goal, simply put to “[e]xamine how political violence is prosecuted in the United States”, and due to the fact that the decision to include cases requires that the defendant have been indicted with a felony crime in the first place (Loadenthal 2018). However, there is plenty of scholarship on the matter to provide insight into the gender disparity that tPP’s database exemplifies.

For example, a recent publication undertaking a similar gendered analysis of political violence argued that “politics and states project masculine power and privilege, with the result that men occupy the dominant social position in politics and women and marginalized men are subordinate” (Ortbals and Poloni-Staudinger 2018). This is the sort of sociological lens which lends itself to understanding why there are so far fewer women in tPP’s database. Because, as a result of this phenomenon, men may more often be perceived as acting with agency, as perpetrators of political crimes, and women may be perceived both by prosecutors and the public as victims or somehow unwilling, unable, or uninvolved, resulting in fewer indictments (Ortbals and Poloni-Staudinger 2018).

Thus this interesting gender disparity may demonstrate both a strength and a limitation of tPP’s database, not by error but rather by design. Regardless, it similarly demonstrates the usefulness of consulting external scholarship which complements and further sheds light on the project’s findings.

– Kayla Groneck


Sources Cited:

Loadenthal, Michael. 2018. “About TPP.” The Terrorism Prosecution Project (blog). 2018. https://tpp.lib.miamioh.edu/about-tpp/.

Ortbals, Candice, and Lori Poloni-Staudinger. 2018. “How Gender Intersects with Political Violence and Terrorism.” Oxford Research Encyclopedia of Politics, February. https://doi.org/10.1093/acrefore/9780190228637.013.308.

Exploring the Post-9/11 Dragnet


This continues our series of student reflections and analysis authored by our research team.


Much of how the West understands the term “terrorism” today is shaped by the events that transpired after 9/11. The Patriot Act was enacted six weeks after the fall of the twin towers, and with it the prerogative of law enforcement officials to arrest and detain thousands of Arab and South Asian men and women, otherwise innocent, on the basis of suspected terrorism. The actions of policymakers and law enforcement officials, in turn, generated the heavily nationalistic and xenophobic paradigm of terrorism in the United States. The average American would go on to associate terrorism with Islam (Pew Research Center 2017; Nisbet & Shanahan 2004), ignoring the nuance of religiosity and the gambit of political ideology that embodies the phenomenon of terrorism.

The Terrorism Prosecution Project (tPP) seeks to dissolve this discourse as we reveal the diverse backgrounds and political ideologies of the perpetrators in various terrorist attacks across the United States from 1990 to present. One recent event that reinforces the absolute vitality of our mission as a team is the terrorist attack that happened in Pittsburgh last week. The attacker, a man named Robert D. Bowers, targeted a Pittsburgh synagogue killing 11 members of the congregation and injuring another 4 (New York Times, 27 October 2018, A1). Anti-Semitic attacks are largely associated with Neo-nazi ideology, and our perpetrator serves as a representation of many of the terrorist attacks that have swept across the United States in which a white, Christian, American-born citizen commits terrible acts of violence against his/her American compatriots.

Interestingly, the likes of Dylann Roof and Robert D. Bowers are not charged with terrorism; rather crimes such as theirs are prosecuted as hate crimes (USAO, 31 October 2018). Why, then, do we have hundreds of names in our database of innocent men and women charged with terrorism when their only crime involved immigration violations, if convicted of any crime at all (tPP database)? The post-9/11 dragnet demonstrates an Orwellian-like undertaking by the United States government to suspend Americans’ civil liberties in the wake of national security concerns. The tPP’s data on those detained during the dragnet reveals that these national security concerns were often unsubstantiated and rooted in a fear for Arab/South Asian Muslims, those who law enforcement believed were most likely to be subject to recruitment to the ranks of Islamic extremist organizations (Abdo 2005, 12; Wong 2006, 164-165).

The “Reason for Inclusion” variable explains why both the likes of Dylann Roof and Robert D. Bowers appear in our dataset as well as the hundreds of people who were arrested during the post-9/11 dragnet. This variable envelops the values of “obvious socio-political aims,” “serves to support organized violence,” and “state speech act.” When a case is inputted into the dataset all three values can be selected or some combination of two of the three if the act of violence or the basis on which the subject was arrested/charged fulfills the criteria for these values. So next we must look at what qualifies each value. For example, Robert D. Bowers would be included in our dataset and coded under the value “obvious socio-political aims;” although his charges were not explicitly labeled as terrorism (USAO, 31 October 2018), the tPP considers his actions terroristic in that his act “represents a form of politically motivated violence intended to communicate a message, in part by the instrumentalization of its victims” (Jackson et al. 2011, 118).

Politically-motivated violence is not always part of a greater whole as we have seen time and again in the United States. For example, the likes of Omar Mateen would be considered an independent perpetrator who had obvious socio-political aims and fell under the state speech act, if he were to be included in our dataset (Omar Mateen is not included because he died before ever being charged with a crime). He would not, however, be coded under “serves to support organized violence” because he was not part of the organized network of ISIS. Similarly, Robert D. Bowers is coded only under “obvious socio-political aims” and not under “serves to support organized violence” because he is not part of an organized neo-Nazi network, such as the Aryan Nations; rather he was inspired to act based on neo-Nazi rhetoric and ideology. He would not be coded under “state speech act,” whereas Omar Mateen would, had he been alive, because the discursive language issued by the state does not describe Bowers as a terrorist. Generally, if the president and/or the DOJ/FBI/DHS describe somebody as a terrorist or have that somebody listed under an official state-issued document charging the subject with “attempt to provide material support to a Foreign Terrorist Organization,” the subject would then be coded in our dataset under a “state speech act,” even if the subject was never convicted. Therefore, a “speech act” has to be an explicit illocutionary act (Searle n.d., 8) made by the state that would indicate the subject is being apprehended on the basis of terrorism or suspected terrorism.

Finally, we’ll hone in on several individuals from the post-9/11 dragnet who are included in our dataset and coded exclusively under the value “state speech act,” but were never convicted of “attempting to provide material support to a Foreign Terrorist Organization.” In one example, a Pakistani man named Ansar Mahmood was arrested after a suspicious construction worker reported him to the FBI and Immigration Naturalization Services (INS). The man had merely wanted to take a picture in front of a reservoir and had asked the worker to do him the favors. The FBI subsequently discovered that Mahmood was housing a young Pakistani couple – Hafiz Tauseef and Aisha Younes – whose visas had expired, and the pair were eventually convicted of having falsified documents (The Times Union 12 October 2001). All three names appear in our dataset because of a state speech act. The DOJ has compiled the names of hundreds who were arrested in the post-9/11 dragnet into a database which explicitly states that the subjects were arrested due to suspected terrorism (DOJ Public/Unsealed Terrorism and Terrorism-Related Convictions 9/11/01-12/31/14). Many ended up being convicted of crimes such as Tauseef’s and Younes’s in which they were charged of “fraud and misuse of visas/permits” (US District Court, Northern District of New York, 17 October 2001) and faced with deportation.

One other example is that of Lofti Raissi, the first arrest made in connection to the 9/11 attacks. Raissi was arrested because he was believed to have trained the 9/11 hijackers to fly planes into the Twin Towers and the Pentagon (The Guardian, 22 November 2009). Though Raissi’s charges were eventually proven to be baseless, the false accusations were mounted against him because of an unfortunate coincidence: Raissi had trained at the same flight school as one of the 9/11 hijackers, Hani Hanjour. Raissi, an Algerian native who is also a Muslim and a trained pilot, was arrested in London after appearing on an FBI watchlist. The US subsequently requested his extradition to the United States on the basis of his alleged connection to the 9/11 hijackers. After five months in prison, Raissi was eventually released and cleared of all charges linking him to the 9/11 attacks. However, his name still appears in our dataset because the United States, at one point in time, labeled him as a terrorist.

The contrast between Bowers’ reason for inclusion in our dataset to the likes of Raissi and Mahmood reveals that there still exists a problematic discourse on terrorism that is propagated by our own state-issued rhetoric and circulated throughout our country. However, while the mission of the TPP, in part, is to deconstruct this rhetoric, we have no sway over how the state chooses to prosecute an individual. Therefore, being able to identify the nuance between the three values that are elemental in our “Reason for Inclusion” variable is pertinent to being able to understand the dataset as a whole.

– Meg


Works Cited

Abdo, Geneive. “Islam in America: Separate but Unequal.” Washington Quarterly, 2005.

Greenwood, Shannon. “How the U.S. General Public Views Muslims and Islam.” Pew Research Center’s Religion & Public Life Project. July 26, 2017. http://www.pewforum.org/2017/07/26/how-the-u-s-general-public-views-muslims-and-islam/.

Jackson, Richard, Lee Jarvis, Jeroen Gunning, and Marie Breen-Smyth. Terrorism: A Critical Introduction. Basingstoke: Palgrave Macmillan, 2011.

Searle, John. “What Is a Speech Act?” In Pragmatics, Discourse Analysis, and Sociolinguistics.

Lyons, Brenda. “Visit to City Reservoir Raised Alarm.” The Investigative Project, October 12, 2001. http://www.investigativeproject.org/documents/misc/518.pdf.

Mele, Christopher. “Pittsburgh Synagogue Shooting Leaves at Least 4 Dead, Official Says.” The New York Times. October 27, 2018. https://www.nytimes.com/2018/10/27/us/active-shooter-pittsburgh-synagogue-shooting.html.

Nisbet, Eric C., and James Shanahan. 2005. Restrictions on Civil Liberties, Views of Islam, and Muslim Americans. Media and Society Group, Cornell University, Dec. 2004.

“Pennsylvania Man Charged with Federal Hate Crimes for Tree of Life Synagogue Shooting.” The United States Department of Justice. October 31, 2018. https://www.justice.gov/usao-wdpa/pr/pennsylvania-man-charged-federal-hate-crimes-tree-life-synagogue-shooting.

USA v. Younes (U.S. District Court, Northern District of New York October 17, 2001).

Wong, Kam C. The USA Patriot Act: A Policy of Alienation. Michigan Journal of Race and Law, 2006.

The impact of 9/11 on terrorism prosecutions


This continues our series of student reflections and analysis authored by our research team.


In my last blog post, I discussed how cases are deemed fit for inclusion in the tPP database. That post ended with a question about when and why crimes without a clear political motive are labeled as terrorism by the government. As the tPP team segued into its analysis phase, this question guided my research.

My suspicion was that the occurrence of 9/11 had a significant impact on the types of cases being prosecuted as terrorism. In my experience coding cases, I spend a lot of time looking at the post-9/11 sweep cases. These usually involve immigration violations by Arabs and are labeled as terroristic by the government. To investigate this further, I divided the data by time periods to look for differences in the data before and after 9/11. I also specifically looked at the year immediately following 9/11.

I focused my analysis on seven variables that could show which crimes are non-violent and non-political. Some markers of these cases are that they are ideologically unaffiliated, the tactic is a non-political criminal violation (such as an immigration violation), and the defendant is “othered.” I used descriptive statistics to look at the frequencies of these traits in cases before and after 9/11, and I used a chi-square analysis to test for significance.

The most noteworthy findings from my analysis are as follows.

  • The frequencies of values for all seven variables I tested were significantly different in the year after 9/11 compared to the whole dataset. This indicates that that year is not representative of all terrorism prosecutions since 1990. The cases in that year are significantly different from the dataset as a whole.
  • Of the seven variables, five of them were also significantly different between the year after 9/11 and the year leading up to it. This accounts for the varying political climates that may affect the larger dataset. This two-year span was subject to the same political and social climate, with the exception of 9/11.
  • There were zero instances of cases with a tactic of a criminal violation not motivated politically prior to 9/11. When there was no political motivation for a crime before 9/11, the government never called it terrorism. In the year following 9/11, 52.4% of cases fell into this tactic category.
  • There were zero instances of deportation in the dataset prior to 9/11. In the year after 9/11, 16.9% of cases ended in deportation.
  • Of all the cases in the dataset ending in deportation, over 60% of them occurred within one year after 9/11. 85.7% of them were defendants who were ideologically unaffiliated. 63.6% of them had a non-political criminal violation tactic.

While my analysis of these cases will continue as the dataset expands, my initial findings seem to confirm my expectations. 9/11 had a significant impact on the government’s prosecution and labeling of terrorism, especially in the immediate aftermath. As I continue to interpret these cases, I plan to look deeper into the long-term implications of 9/11 for non-political crimes.

Lauren Donahoe is a senior biology major and a senior team member of the Prosecution Project. She has been with the project since Fall 2017.

Further Exploration of the Role Played by Pleas


This continues our series of student reflections and analysis authored by our research team.


In my previous blog post, I explained the occurrence of pleas and why defendants might choose to plead guilty, including financial, time, or other risk factors. This blog post will expand upon that specifying in the category of guilty plea bargains. I did statistical analysis between cases that took plea bargains (we’ll call them PB cases) and non-plea bargain cases (NPB cases) to look for discrepancies within any of the variables between the two sets of cases.

From what I found, there were no discrepancies except for the variable “tactic” which includes condensed categories such as: CBRN (Chemical, Biological, Radiological or Nuclear defense weapons,) Conspiracy (i.e Conspiracy: murder, Conspiracy: material/financial support, etc.,) Direct Person-to-Person violence (i.e Assault, Beheading, Murder, Kidnapping, etc.,) Explosives/Arson (i.e IID, IED, military/commercial explosives,) Non-physical violence (i.e Material/financial support, Perjury/Obstruction of Justice, etc.,) Not linked/Unknown, and Various methods.

I chose to look further into this disjuncture and see if there were any significant findings between the relation of tactic variables and PB/NPB. What I found is that there was a significant relation between NPB cases and all of the tactics.

  • 71.4% of tPP’s cases were NPB cases, compared to only 26% being PB cases, and consequently in every single tactic variable mentioned above, NPB were the most frequent pleads.

When looking into outside literature, I found that the most common discrepancy between plea bargains and other variables found by other academics was race. In Carlos Berdejó’s findings within Criminalizing Race: Racial Disparities in Plea-Bargaining, he found that “white defendants are 25% more likely than black defendants to have their most serious initial charge dropped or reduced… [as well as] 75% more likely… to be convicted for crimes carrying no possible incarceration.” (2018)

I wanted to see if this finding was similar to the cases within tPP’s dataset, and my statistical calculations proved this to be true: 28.7% of white defendants were offered plea bargains by the court, while only 10.1% of black defendants were. Combining this finding within tPP’s dataset and outside literature which put blame on police racial bias, it can be concluded that white defendants take plea bargains at almost triple the rate that African Americans do within tPP’s data, and this is likely a consequence of racial bias within our criminal justice system.

– Isabella


References

Berdejó, Carlos. “Criminalizing Race: Racial Disparities in Plea Bargaining.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, September 13, 2017. https://papers.ssrn.com/abstract=3036726.