Class 11: Responses to Political Violence

State responses: Policies

Dr. Michael C. Zeller

Opening notes

Open Stickerfrom Open Stickers

short synopsis for final essay due Friday (17 January) (send to me via email)

State policy responses

What options/possibilities are there
One policy example
deradicalisation programmes
- Membrives and Alonso (2022)

They Can Reform Their Policies Make Changes GIFfrom They Can Reform Their Policies GIFs

Starting question

What policies can states apply to address (potential) political violence? Can we categorise them somehow?

One policy example

Does anyone recognise this site?

One policy example

Valley of the Fallen (incl., Catholic basilica), outside Madrid
monument constructed under Franco, using forced/convict labour
burial place for Franco (exhumed 24.10.2019) and Primo de Rivera (exhumed 23.4.2023)
Ley de Memoria Histórica: recognises and broadens “the rights and establishes measures in favour of those who suffered persecution or violence during the civil war and the dictatorship.”

Deradicalisation programmes

definition: initiatives to help individuals stop extremist behaviour and abandon extremist beliefs
common elements:
1. mixed programme elements
2. credible deradicalisation
3. post-treatment follow-up
4. create commitments (to family, work, etc.)
5. material inducement (but not relying on this)

Deradicalisation programme - Indonesia

Membrives and Alonso (2022, 19) - the aim of derad./exit programmes

The Radicalization Awareness Network evaluation of exit programs understands that “success for exit programs usually consists of disengagement (leaving a radical environment and violent behaviour), deradicalization (leaving a radical ideology), functional integration (such as housing, employment and health care) and social reintegration (family, friends, community) in the long term.

Membrives and Alonso (2022) - research approach

RQ: what are the challenges of intervening with juvenile offenders involved in terrorism?

cases: five radicalized minors from Spain who were convicted of membership of a jihadist terrorist organisation, now engaged in derad. programmes
data: semi-structured interviews
four stages of intervention
1. evaluation of the youths’ risks and needs
2. hypothesis formulation of each individual case
3. design and implementation of objectives, actions, and activities
4. final evaluation

Membrives and Alonso (2022) - initial risk evaluation

what do you think of these ‘factors’ for assessing risk?

Factor	Nadia	Oscar	Dafya	Thamir	Caleb
1. Past and present offenses and sentences	low	low	low	low	low
2. educational guidelines in family environment	moderate	moderate	low	moderate	moderate
3. formal education/ employment	high	moderate	moderate	low	low
4. relationship with peer group	moderate	high	low	high	high
5. Substance misuse	low	moderate	low	low*	low*
6. leisure/hobbies	high	high	moderate	high	high
7. Personality/ behavior	moderate	moderate	moderate	moderate	moderate
8. Attitudes, values, and beliefs	moderate	high	moderate	high	high
General risk level	21	22	5	19	18
	from moderate (9–22) to very high	from moderate (9–22) to very high	from low (0–8) to moderate	from moderate (9–22) to very high	from moderate (9–22) to very high

Membrives and Alonso (2022) - activities

why these activities for a derad. programme? what’s the reasoning? do you think it would be effective?

Area	Activity	Nadia	Oscar	Dafya	Thamir	Caleb
formative	School classroom at the Center	X	X	X	X	X
formative	School support and encouragement for reading			X		X
formative	Vocational training			X
Pre-work	Book binding	X	X		X
Pre-work	restoration	X	X			X
Pre-work	pottery	X
Pre-work	modeling, sculpture, decoration			X		X
Pre-work	handcrafted wood turning				X	X
Personal development and social competence	Central educational and therapeutic treatment Program for Young offenders	X	X	X	X	X
Personal development and social competence	Program on Personal development and Social Competence	X	X	X	X	X
Personal development and social competence	Program on Personal development and Civic-ethic education	X	X	X	X	X
Personal development and social competence	Program on Preparation for an independent life		X
Personal development and social competence	Workshop on equal opportunities	X		X	X
occupational workshop	dance workshop	X
occupational workshop	Painting on canvas, decoration, models	X	X	X	X	X
leisure and free time	Sports	X	X	X	X	X
leisure and free time	Video-forum	X	X	X	X	X
leisure and free time	Group leisure	X	X	X	X	X
leisure and free time	individual leisure	X	X	X	X	X

Membrives and Alonso (2022) - activities rated according to

activities rated according to…

Implementation: efforts have been made by the practitioners who remain awaiting the assimilation and response by the minor;
Development: the minor has assimilated ideas and has begun to respond making some efforts;
Reinforcement: the minor has made considerable progress, but it is necessary to continue supervising him/her;
Finalized: the minor has met the objective;
Interrupted: it has not been possible to work on the objective and/or activity.

Membrives and Alonso (2022) - takeaways

Traditional objectives of criminal justice must give way to rehabilitation and restorative justice objectives when dealing with minors.
- do you agree or disagree? This is an ongoing debate in public policy

The Spanish judicial framework provides the framework for the judicial response against offences committed by youths in Spain, with the guiding principles of any intervention being its sanctioning-educational nature and the superior interest of the minor.

Poll: state policies

A QR code for the survey.

Take the survey at https://forms.gle/WskY6kmGLEjXvo586

policy approach to pacify ethnic/sectarian conflict?
de-radicalisation programmes more successful than punishment?
criminalising hate speech and incitement?

most important support during disengagement?
effective policy measures preventing radicalisation?
participation in de-radicalisation programmes be mandatory?
should states fund religious or ideological mentors?

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-1vSPyp6CIK_YgBLLJa_9102x68NIz6cjOfC67UTad0alyXY6cngKS6fOdxzh4zAG4WV0YtHx7_P8eZ34/" +
    "pub?gid=1695767618&single=true&output=csv",
  10000, 1, 8); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

policy approach to pacify ethnic/sectarian conflict?

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

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

plot_lasting_peace = Plot.plot({
  marks: [
    Plot.barY(lasting_peaceCounts, {
      x: "lasting_peace",
      y: "count",
      fill: "lasting_peace",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "amnesty",
      "power-sharing governance",
      "reconciliation commissions",
      "intergroup dialogue",
      "other"
    ],
    range: [
      "indigo",
      "goldenrod",
      "cadetblue",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 500,
  x: { label: "", tickSize: 2, tickRotate: -60, padding: 0.2,
    domain: ["amnesty","power-sharing governance","reconciliation commissions","intergroup dialogue", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(lasting_peaceCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, lasting_peace_maxCountRE]
  },
  facet: { data: lasting_peaceCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Poll results: state policy responses

de-radicalisation programmes more successful than punishment?

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

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

plot_derad_success = Plot.plot({
  marks: [
    Plot.barY(derad_successCounts, {
      x: "derad_success",
      y: "count",
      fill: "derad_success",
      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(derad_successCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, derad_success_maxCountRE]
  },
  facet: { data: derad_successCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

most important support during disengagement?

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

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

plot_disengage_support = Plot.plot({
  marks: [
    Plot.barY(disengage_supportCounts, {
      x: "disengage_support",
      y: "count",
      fill: "disengage_support",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "education",
      "employment",
      "family",
      "mentorship",
      "other"
    ],
    range: [
      "indigo",
      "goldenrod",
      "cadetblue",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 500,
  x: { label: "", tickSize: 2, tickRotate: -60, padding: 0.2,
    domain: ["education", "employment", "family", "mentorship", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(disengage_supportCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, disengage_support_maxCountRE]
  },
  facet: { data: disengage_supportCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Poll results: state policy responses

effective policy measures preventing radicalisation?

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

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

plot_preventing_rad = Plot.plot({
  marks: [
    Plot.barY(preventing_radCounts, {
      x: "preventing_rad",
      y: "count",
      fill: "preventing_rad",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "community social programmes", 
      "job opportunities", 
      "programmes on tolerance", 
      "restricting extreme content",
      "other"
    ],
    range: [
      "indigo",
      "goldenrod",
      "cadetblue",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 500,
  x: { label: "", tickSize: 2, tickRotate: -60, padding: 0.2,
    domain: ["community social programmes", "job opportunities", "programmes on tolerance", "restricting extreme content", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(preventing_radCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, preventing_rad_maxCountRE]
  },
  facet: { data: preventing_radCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

criminalising hate speech and incitement?

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

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

plot_hate_speech = Plot.plot({
  marks: [
    Plot.barY(hate_speechCounts, {
      x: "hate_speech",
      y: "count",
      fill: "hate_speech",
      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(hate_speechCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, hate_speech_maxCountRE]
  },
  facet: { data: hate_speechCounts, x: "measure", label: "" },
  marginLeft: 140,
  style: {
    width: 1350,
    height: 500,
    fontSize: 30,
  }
});

Poll results: state policy responses

participation in de-radicalisation programmes be mandatory?

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

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

plot_mandatory_derad = Plot.plot({
  marks: [
    Plot.barY(mandatory_deradCounts, {
      x: "mandatory_derad",
      y: "count",
      fill: "mandatory_derad",
      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(mandatory_deradCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, mandatory_derad_maxCountRE]
  },
  facet: { data: mandatory_deradCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

should states fund religious or ideological mentors?

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

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

plot_mentors = Plot.plot({
  marks: [
    Plot.barY(mentorsCounts, {
      x: "mentors",
      y: "count",
      fill: "mentors",
      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(mentorsCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, mentors_maxCountRE]
  },
  facet: { data: mentorsCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

criminal justice responses

criminal justice responses to political violence
Koehler (2019) - Violence and Terrorism from the Far-Right: Policy Options to Counter an Elusive Threat

Remember The Titans Cruel And Unusual Punishment GIFfrom Remember The Titans GIFs

criminal justice responses to political violence - an example (Taylor and Suliman 2021)

21-years-old British man, Ben John, with neo-Nazi sympathies
collected extremist documents (including a book that contains bomb-making instructions, illegal in the UK) on computer
- possessing information likely to be useful for preparing an act of terrorism, punishable by up to 15 years in prison under Section 58 of UK’s Terrorism Act

recommended sentencing? what do you think? (keeping in mind British judges have lots of leeway with sentencing)

sentenced by judge to read works of great literature by Jane Austen, William Shakespeare, Thomas Hardy and Charles Dickens
appeals court overturns ruling, sentenced to two years in prison

Koehler (2019) - EU context

Comparing right-wing extremist terrorism to other forms

Even though the number of arrests related to right-wing extremist terrorism within the European Union almost doubled to 20 in 2017 from 12 in 2016, it pales in comparison to other forms of political violence, at least in terms of numbers (e.g. 705 jihadists in 2017).

Koehler (2019) - focusing in on Germany

RWE explosives & arson crimes (bars); all RWE violent crimes (line)

Looking at … explosives attacks or arson, crimes usually but not always (legally) framed as terrorism, statistics from Germany indicate a surge during the so-called “refugee crisis” between 2015 and 2016. …

Koehler (2019) - focusing in on Germany, homicides

Recognised by BRD vs. identified by Amadeu Antonio Stiftung

Koehler (2019) - focusing in on Germany, homicides

library(tmap)
library(tidyverse)
library(sf)
library(rnaturalearth)
library(gganimate)
library(gifski)

dfALLgrouped <- dfALL %>% 
  group_by(YEAR)

dfALLgrouped <- as.data.frame(dfALLgrouped)

dfALLgrouped$Latitude <- dfALLgrouped$LAT
dfALLgrouped$Longitude <- dfALLgrouped$LON

dfALLgrouped$LATjit <- jitter(dfALLgrouped$LAT, factor = 300)
dfALLgrouped$LONjit <- jitter(dfALLgrouped$LON, factor = 300)

dfALLgrouped_sf <- dfALLgrouped %>% drop_na(LAT)

dfALLgrouped_sf <- dfALLgrouped_sf %>% 
  st_as_sf(
    coords = c("LONjit", "LATjit"),
    crs = st_crs("EPSG:32632") # CRS for Germany
  )

DE = ne_states(returnclass = "sf") |>
  filter(admin == "Germany") 

st_crs(dfALLgrouped_sf) <- st_crs(DE)

fr_hom_anim <- tm_shape(DE)+
  tm_polygons(fill="gold", col="darkolivegreen")+
  tm_shape(dfALLgrouped_sf)+
  tm_bubbles(shape=21, 
             size=5, 
             col="CATEGORY", 
             palette = c("Official" = "red","Unofficial" = "blue"),
             scale=8, 
             alpha=0.5, 
             border.col="black")+
  tm_facets(pages="YEAR")+
  tm_layout(legend.show = FALSE,
            panel.label.size = 4, 
            panel.label.color = 'black',
            panel.label.bg.color = 'grey', 
            panel.label.height = 3,
            legend.text.size = 1.5)
tmap_animation(
  fr_hom_anim, 
  filename = "fr_hom_anim.gif",
  delay = 50, 
  width = 1200, 
  height = 1200
)

o: recognised by BRD
o: identified by Amadeu Antonio Stiftung

Koehler (2019) - focusing in on Germany, homicides

o: officially recognised by BRD
o: identified by Amadeu Antonio Stiftung

Koehler (2019) - focusing in on Germany, law enforcement

Ermittlungsverfahren/opened 129 case
closed/discontinued 129 case
charges in 129 case
verdict/conviction in 129 case
compare: jihadist terrorism in 2017: 952 opened cases, 460 closed

§129: forming or membership in criminal or terrorist org.
more terror investigations paralleling spikes in right-wing extremist violence
- 2012 (15) spike, reaction to NSU
- i.e., triggered by political and popular pressure

“However, even if keeping in mind that one group could have been responsible for more than one arson or explosive attack, the number of new investigations still falls short of the overall level of severe violence (four and six new investigations in 2016 and 2017).”

debate raised by Koehler (2019): hate crime - terrorism

hate crimes: “a criminal act that is motivated by a bias toward the victim or victims real or perceived identity group”, may include the desire to “terrorize a broader group”

debate raised by Koehler (2019): hate crime - terrorism

RWE hate crimes - terrorism: close cousins
- “the target of an offense is selected because of his or her group identity, not because of his or her individual behaviour, and because the effect of both is to wreak terror on a greater number of people than those directly affected by violence”
RWE hate crimes - terrorism: distant relatives
- terrorism: lots of planning, usually; actively seek publicity
- hate crimes: more spontaneous, usually; seldom seek publicity
RWE violence can be both
- depending on the degree to which it pursues political and social objectives (prerequisite for terrorism—not for hate crimes)

what does your expertise tell you? what is your position?

Koehler (2019): selected recommendations

Avoid double standards between forms of political violence
allocate adequate resources to counter RWE
appropriate judicial responses (‘quick and efficient’)
increase funding for research on FR violence and terrorism
acknowledge relationship between hate crimes and terrorism
- reporting mechanisms about right-wing terrorism should not only be based on legal prosecutions and convictions using the ‘terrorism’ label, but also consider psychological effects on the target group of violent acts and specific attack forms used.
expand EXIT programmes for RWE

what do you make of these recommendations (or others)?

Democracy-building policy example

Demokratie leben! - ‘practising and maintaining democratic culture’

finances projects all over Germany that develop and trial new ideas and innovative approaches in promoting democracy, encouraging diversity, and preventing extremism.

Snapshot: stopping ethnic violence

Varshney (2001): Hindu-Muslim violence in India (urban and locally concentrated)—breakouts of violence in some cities (e.g., Aligarh) and not others (e.g., Calicut)—why?
- Calicut:
  - ‘peace committees’ work across communities, quash rumours
  - Hindus and Muslims: 84% visit each other regularly; 83% eat together; 90% children play together
- Aligarh:
  - peace committees tend to be intrareligious, not interreligious
  - Hindus and Muslims: 60% visit each other regularly; 54% eat together; 42% children play together

Snapshot: stopping ethnic violence

Varshney (2001): Hindu-Muslim violence in India (urban and locally concentrated)—breakouts of violence in some cities (e.g., Aligarh) and not others (e.g., Calicut)—why?

The preexisting local networks of civic engagement between the two communities stand out as the single most important proximate explanation for the difference between peace and violence.

This argument, it should be clarified, is probabilistic, not lawlike.

Darby (1986): sectarian violence in Belfast communities (Kileen/Banduff, Upper Ashbourne Estates) and absence (Dunville)
- Dunville: non-segregated service, sport, and parent clubs

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

Darby, John. 1986. Intimidation and the Control of Conflict in Northern Ireland. Syracuse, NY: Syracuse University Press.

Koehler, Daniel. 2019. “Violence and Terrorism from the Far-Right: Policy Options to Counter an Elusive Threat.” Policy {{Brief}} February. The Hague: International Centre for Counter-Terrorism. https://www.jstor.org/stable/pdf/resrep19617.pdf.

Membrives, María Teresa García, and Rogelio Alonso. 2022. “Countering Violent Extremism in Spain: Analyzing the Intervention with Young Jihadi Convicted of Membership of a Terrorist Organization.” Studies in Conflict & Terrorism, November, 1–33. https://doi.org/10.1080/1057610X.2022.2143741.

Taylor, Adam, and Adela Suliman. 2021. “British White Supremacist , Initially Sentenced to Read Austen and Dickens , Imprisoned for Two Years.” The Washington Post, January.

Varshney, Ashutosh. 2001. “Ethnic Conflict and Civil Society: India and Beyond.” World Politics 53 (3): 362–98.