Class 7: Politically Violent Activity

Foreign fighters and political violence in authoritarian contexts

Opening notes

Presentation groups

Presentations line-up
Date	Presenters	Method
4 Dec:	Shahadaan, Kristine, Daichi	ethnography
11 Dec:	Bérénice, Zorka, Victoria, Katharina	TBD
18 Dec:	Shoam, Aidan, Tara, Sebastian	QCA

Foreign fighers

definition
examples

Aircraft Airplane Stickerfrom Aircraft Stickers

Foreign fighters

foreign fighters - individuals who travel to a conflict zone from another territory (prima facie evidence of radicalism \(\rightarrow\) engagement in political violence; a ‘security failure’ by authority of origin state?)
examples historically? from cases you know of?

Foreign fighters examples

Foreign fighters

foreign fighters - individuals who travel to a conflict zone from another territory (prima facie evidence of radicalism \(\rightarrow\) engagement in political violence; a ‘security failure’ by authority of origin state?)
examples historically? from cases you know of?

FF is a widespread part and enduring element of political violence across the world

Poll: foreign fighters

A QR code for the survey.

Take the survey at https://forms.gle/JJ7ufLwrLJYKAbvg8

should states have laws banning travel to conflict zones?
most effective for addressing foreign fighters phenomenon?
top policy priority for dealing with returning fighters?

are returning foreign fighters a significant threat?
should states be able to revoke fighters’ citizenship?

md`## Poll results (Respondents: ${respondentCount})`

import { liveGoogleSheet } from "@jimjamslam/live-google-sheet";
import { aq, op } from "@uwdata/arquero";

// UPDATE THE LINK FOR A NEW POLL
surveyResults = liveGoogleSheet(
  "https://docs.google.com/spreadsheets/d/e/" +
    "2PACX-1vTCOL_6dhwjrpQW9BGa1-p-uRlIknFTjWEkgBgHyaML3dziFgSxIgMU1pUmTfxJyHHm27Jv4_ZZUYT3/" +
    "pub?gid=164736256&single=true&output=csv",
  10000, 1, 6); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

laws banning travel to conflict zones?

banning_travelCounts = aq.from(surveyResults)
  .select("banning_travel")
  .groupby("banning_travel")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
banning_travel_maxCountRE = Math.max(...banning_travelCounts.objects().map(d => d.count));

plot_banning_travel = Plot.plot({
  marks: [
    Plot.barY(banning_travelCounts, {
      x: "banning_travel",
      y: "count",
      fill: "banning_travel",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Yes",
      "No",
      "Maybe"
    ],
    range: [
      "forestgreen",
      "darkred",
      "goldenrod"
    ]
  },
  marginBottom: 80,
  x: { label: "", tickSize: 2, tickRotate: -1, padding: 0.2,
    domain: ["Yes", "No", "Maybe"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(banning_travelCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, banning_travel_maxCountRE]
  },
  facet: { data: banning_travelCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

most effective for addressing foreign fighters phenomenon?

effective_addressingCounts = aq.from(surveyResults)
  .select("effective_addressing")
  .groupby("effective_addressing")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
effective_addressing_maxCountRE = Math.max(...effective_addressingCounts.objects().map(d => d.count));

plot_effective_addressing = Plot.plot({
  marks: [
    Plot.barY(effective_addressingCounts, {
      x: "effective_addressing",
      y: "count",
      fill: "effective_addressing",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "border security", 
      "social work programmes", 
      "block recruitment/propaganda", 
      "other"
    ],
    range: [
      "darkred",
      "goldenrod",
      "cadetblue",
      "gray"
    ]
  },
  marginBottom: 400,
  x: { label: "", tickSize: 2, tickRotate: -40, padding: 0.2,
    domain: ["border security", "social work programmes", "block recruitment/propaganda", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(effective_addressingCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, effective_addressing_maxCountRE]
  },
  facet: { data: effective_addressingCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Who becomes a foreign fighter, how are returnees addressed

Morris (2023) - Who Becomes a Foreign Fighter? Characteristics of the Islamic State’s Soldiers
- context, casing, data, locales of fighter mobilisation
- results and findings
dealing with returning foreign fighters

Morris (2023) - Context

MENA (Middle East, North Africa) countries have sent the largest numbers of foreign fighters to Syria

Why is this the case? What are the relevant contextual factors?

Morris (2023) - Context

MENA (Middle East, North Africa) countries have sent the largest numbers of foreign fighters to Syria

Why is this the case? What are the relevant contextual factors?
- religious ideology (jihad)
- religious internationalism (history of MENA alliance, e.g., in wars and campaigns against Israel)
- contexts of deprivation? travelling to fight as a ‘way out’

Morris (2023) - Casing

Three countries analysed: Morocco, Egypt, and Turkey
- Morocco represents foreign fighters from the Maghreb (Northwest Africa). (representative country context)
- Egyptian foreign fighters are more alike to ISIS recruits from the Levant than North Africa. (representative country context)
- Turkey,
  - Turkey’s historical, political, and socioeconomic traits make it as a (least likely mobilisation context)
  - Turkish foreign fighters may be more like Islamic State members from the Middle East and Central Asia than the other two countries. (representative country context)
NB: Two casing logics at work here: (1) thinking in terms of population of foreign fighters and clusters therein and (2) thinking in terms of country cases in comparison with other country cases

Morris (2023) - data

data from leaked IS personnel records
explanatory variables
- occupation: unemployed, unskilled, student, skilled
- education: none, primary, secondary, university
- marital status: single, married
- children: yes, no
- age
any major factors that seem omitted?

Morris (2023) - locales of fighter mobilisation

Major centres: Tangier, Tetouan, and surrounding region, and Fes; Cairo, Alexandria, and lower Nile area; Istanbul and Antep

any visualisation problems?

Morris (2023) - regression results (how to interpret?)

Reading a regression table

Remember: regression is a tool for understanding a phenomenon as a linear function (generally) → (y = mx + b)

Numbers not in parentheses next to a variable: regression coefficient: expected change in DV for a one-unit increase in IV. NB: ositive or negative relationship?
Numbers inside parentheses next to a variable: standard error: estimate of the standard deviation of the coefficient
Asterisks/‘stars’: statistical significance: probability of results as extreme as observed result, under the assumption that the null hypothesis is correct. Smaller p-value means such an observation would be less likely under null hypothesis; hence, significance. Statistical significance suggests more precise estimates—NOT necessarily that one IV is more important than another.

Morris (2023) - regression results (how to interpret?)

a university education is the strongest correlate of joining a terrorist organization.

Morris (2023) - regression results

Findings

Despite the difference in space and time, ISIS foreign fighters have a similar profile to members of Hamas, Hezbollah, the Palestinian Islamic Jihad, and even violent political activists in Bengal. The men in all these samples are predominately male, well-educated, urban, unmarried, and young.

a university education is the strongest correlate of joining a terrorist organization.

any of characteristics stand out for… theoretical reasons? compared to other cases you know?
IS attracted both unskilled and skilled workers = offered to improve the livelihood of its members

Dealing with returning foreign fighters (1)

Spain example

Dealing with returning foreign fighters (2)

Economist (a bit sensationalist and some graphic imagery)

Dealing with returning foreign fighters (3)

David Malet

Poll results - returning foreign fighters

top policy priority for dealing with returning fighters?

top_priorityCounts = aq.from(surveyResults)
  .select("top_priority")
  .groupby("top_priority")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
top_priority_maxCountRE = Math.max(...top_priorityCounts.objects().map(d => d.count));

plot_top_priority = Plot.plot({
  marks: [
    Plot.barY(top_priorityCounts, {
      x: "top_priority",
      y: "count",
      fill: "top_priority",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "prosecution",
      "surveillance",
      "deradicalisation programmes",
      "reintegration service",
      "deportation",
      "citizenship revocation",
      "other"
    ],
    range: [
      "indigo",
      "goldenrod",
      "darkred",
      "cadetblue",
      "darkorange",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 500,
  x: { label: "", tickSize: 2, tickRotate: -60, padding: 0.2,
    domain: ["prosecution", "surveillance", "deradicalisation programmes", "reintegration service", "deportation", "citizenship revocation", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(top_priorityCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, top_priority_maxCountRE]
  },
  facet: { data: top_priorityCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

are returning foreign fighters a significant threat?

threatCounts = aq.from(surveyResults)
  .select("threat")
  .groupby("threat")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
threat_maxCountRE = Math.max(...threatCounts.objects().map(d => d.count));

plot_threat = Plot.plot({
  marks: [
    Plot.barY(threatCounts, {
      x: "threat",
      y: "count",
      fill: "threat",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Strongly disagree",
      "Disagree",
      "Neutral",
      "Agree",
      "Strongly agree"
    ],
    range: [
      "red",
      "pink",
      "lightgrey",
      "lightgreen",
      "forestgreen"
    ]
  },
  marginBottom: 180,
  x: { label: "", tickSize: 2, tickRotate: -45,
    domain: ["Strongly disagree", "Disagree", "Neutral", "Agree", "Strongly agree"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(threatCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, threat_maxCountRE]
  },
  facet: { data: threatCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

should states be able to revoke fighters’ citizenship?

revokeCounts = aq.from(surveyResults)
  .select("revoke")
  .groupby("revoke")
  .count()
  .derive({ measure: d => "" })

// Calculate the maximum count from your dataset
revoke_maxCountRE = Math.max(...revokeCounts.objects().map(d => d.count));

plot_revoke = Plot.plot({
  marks: [
    Plot.barY(revokeCounts, {
      x: "revoke",
      y: "count",
      fill: "revoke",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "Strongly disagree",
      "Disagree",
      "Neutral",
      "Agree",
      "Strongly agree"
    ],
    range: [
      "red",
      "pink",
      "lightgrey",
      "lightgreen",
      "forestgreen"
    ]
  },
  marginBottom: 180,
  x: { label: "", tickSize: 2, tickRotate: -45,
    domain: ["Strongly disagree", "Disagree", "Neutral", "Agree", "Strongly agree"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(revokeCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, revoke_maxCountRE]
  },
  facet: { data: revokeCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Political violence in an authoritarian contexts

questions
Enstad (2018) - Right-Wing Terrorism and Violence in Putin’s Russia
- data overview

Authoritarian Wayne'S World GIFfrom Authoritarian GIFs

Political violence in an authoritarian context

What are the special conditions of political violence in within an authoritarian regime?

more extreme possibilities of repression

against the state or against others?

combatted by, tolerated by, or supported by the state?

Enstad (2018) - an empirical puzzle?

Putin’s Russia has seen much more right-wing violence than any other comparable country in the past 25 years.

Enstad (2018) - aim and data

descriptive research aim: comparison of countries and regions, patterns and trends

The RTV-RUSSIA dataset currently consists of 495 events, including 406 deadly events causing 459 deaths over a period of eighteen years (2000-2017). RTV-RUSSIA has been patterned on the RTV dataset (RightWing Terrorism and Violence in Western Europe) compiled by Jacob Ravndal,[4] and features the same set of variables (date, location, event type, perpetrator type, perpetrator’s organizational affiliation, victim, weapon(s) used, number of casualties, as well as a description of the event).

Enstad (2018) - data overview

	Type of violence
Perpetrator type	Premeditated attacks	Spontaneous attacks	Plots	Preparation for armed struggle	Unknown	Sum
Organized groups	48	3	2	0	1	54
Affiliated members	10	1	2	0	1	14
Autonomous cells	34	0	7	0	0	41
Gangs	121	12	0	0	2	135
Unorganized	74	15	1	0	1	91
Lone actors	16	8	2	1	1	28
Shadow groups	0	0	0	0	1	1
Unknown	104	7	2	1	16	130
Total	408	46	16	2	23	495

Enstad (2018) - region comparison

Enstad (2018) - longitudinal comparison

Enstad (2018) - findings

Russian activists have operated much more violently compared to their counterparts in the United States and Western Europe.
Russian activists have operated more purposefully, with premeditated attacks vastly outnumbering spontaneous ones.
gangs and unorganized groups have been the most common perpetrators; the overall share of lone actors has been much lower than in the West

Any questions, concerns, feedback for this class?

Anonymous feedback here: https://forms.gle/NfF1pCfYMbkAT3WP6

Alternatively, please send me an email: m.zeller@lmu.de

References

Enstad, Johannes Due. 2018. “Right-Wing Terrorism and Violence in Putin’s Russia.” Perspectives on Terrorism 12 (6): 89–103.

Morris, Andrea Michelle. 2023. “Who Becomes a Foreign Fighter? Characteristics of the Islamic State’s Soldiers.” Terrorism and Political Violence, January, 1–19. https://doi.org/10.1080/09546553.2022.2144730.