Radicalisation
Date | Presenters | Method |
---|---|---|
3 July: | Alexander V., Luis G., Oscar O., Mia C. | descriptive inference |
10 July: | Lina S., Stephen W., Philomena B., Aarón Z. | ethnography |
17 July: | Corinna Z., Eva M., and Rostislav N. | TBD |
24 July: | Sebastian K., Thomas R., Emilia Z., Florian P. | TBD |
24 July: | Lorenz F., Daniel B., Medina H. | quant. text analysis |
Date | Presenters | Method |
---|---|---|
15 May: | Idil M., Zeynep P., Liesl W., Selin K., Chiara W. | logistic regression |
22 May: | Gabriel W., Lina M., Florian S., Julian B. | discourse analysis |
29 May: | NO CLASS MEETING |
Date | Presenters | Method |
---|---|---|
5 June: | Rasmus B., Andre D., Josefine E., Ioanna L., Santiago C. | surveys |
12 June: | Omar B., Lela E., Niclas W. | network analysis |
19 June: | NO CLASS MEETING | |
26 June: | Colombe I., Konstantin S., Jakob W., Veronika L. | ethnography |
26 June: | Maksim K., Felix S., Jon L.D., Damir S., Korbinian M. | case study |
radicalisation (change in belief): process of connecting with and adopting radical or extremist ideology—does not necessarily result in violence or ‘engaging’ in extremist activity
engagement (change in behaviour): (in this context) process or act of performing radical or extremist activity, especially violence
deradicalisation (change in belief): “process by which an individual is diverted from an extremist ideology, eventually rejecting an extremist ideology and moderating their beliefs” (Gaudette, Scrivens, and Venkatesh 2022, 1)
disengagement (change in behaviour): “process by which an individual decides to leave their associated extremist group or movement in order to reintegrate into society” (Ibid.)
ideological activists, motivated by ideas; drifters and fellow travelers, seeking friendship; and frustrated youth with criminal records
key finding: radicals tend to have normal personalities (pathologising is futile)
external
relational
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identity
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personal
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psychological
personal
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psychological
psychological
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relational
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personal
psychological
personal
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relational
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identity
psychological
personal
identity
relational
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relational
psychological
personal
identity
relational
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psychological
personal
identity
relational
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See further in (e.g.) Campelo et al. (2018), della Porta (2018), Fahey and Simi (2019), Jensen, Seate, and James (2018), Vergani et al. (2018)
psychological
personal
identity
relational
external
But…
But…
No support was garnered for the identification of distinct pathways of homogeneous risk factors among either sample of violent offenders.
Take the survey at https://forms.gle/nqYxotpf7phB8fYP8
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-1vR_GvgDzmO4AX9VAGayfBTMlR9W2Z6bF-IYePcQSuLN8m5M1vfjTIEY0bJu38Ql2MMzIy9mJSUpGsWu/" +
"pub?gid=1855864627&single=true&output=csv",
10000, 1, 7); // adjust the last number to select all relevant columns
respondentCount = surveyResults.length;
Is there a difference between how individuals radicalise depending on ideology?
differenceCounts = aq.from(surveyResults)
.select("difference")
.groupby("difference")
.count()
.derive({ measure: d => "" })
// Calculate the maximum count from your dataset
difference_maxCountRE = Math.max(...differenceCounts.objects().map(d => d.count));
plot_difference = Plot.plot({
marks: [
Plot.barY(differenceCounts, {
x: "difference",
y: "count",
fill: "difference",
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(differenceCounts.objects().map(d => d.count))
).sort((a, b) => a - b),
domain: [0, difference_maxCountRE]
},
facet: { data: differenceCounts, x: "measure", label: "" },
marginLeft: 60,
style: {
width: 1600,
height: 500,
fontSize: 40,
},
});
Younger more vulnerable to radicalisation?
youngerCounts = aq.from(surveyResults)
.select("younger")
.groupby("younger")
.count()
.derive({ measure: d => "" })
// Calculate the maximum count from your dataset
younger_maxCountRE = Math.max(...youngerCounts.objects().map(d => d.count));
plot_younger = Plot.plot({
marks: [
Plot.barY(youngerCounts, {
x: "younger",
y: "count",
fill: "younger",
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(youngerCounts.objects().map(d => d.count))
).sort((a, b) => a - b),
domain: [0, younger_maxCountRE]
},
facet: { data: youngerCounts, x: "measure", label: "" },
marginLeft: 60,
style: {
width: 1600,
height: 500,
fontSize: 40,
},
});
Social media changing radicalisation?
social_mediaCounts = aq.from(surveyResults)
.select("social_media")
.groupby("social_media")
.count()
.derive({ measure: d => "" })
// Calculate the maximum count from your dataset
social_media_maxCountRE = Math.max(...social_mediaCounts.objects().map(d => d.count));
plot_social_media = Plot.plot({
marks: [
Plot.barY(social_mediaCounts, {
x: "social_media",
y: "count",
fill: "social_media",
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(social_mediaCounts.objects().map(d => d.count))
).sort((a, b) => a - b),
domain: [0, social_media_maxCountRE]
},
facet: { data: social_mediaCounts, x: "measure", label: "" },
marginLeft: 60,
style: {
width: 1600,
height: 500,
fontSize: 40,
},
});
Dr. Erin Saltman talk (until 8.00 mark)
Gov. programmes effective intervention?
government_programmesCounts = aq.from(surveyResults)
.select("government_programmes")
.groupby("government_programmes")
.count()
.derive({ measure: d => "" })
// Calculate the maximum count from your dataset
government_programmes_maxCountRE = Math.max(...government_programmesCounts.objects().map(d => d.count));
plot_government_programmes = Plot.plot({
marks: [
Plot.barY(government_programmesCounts, {
x: "government_programmes",
y: "count",
fill: "government_programmes",
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(government_programmesCounts.objects().map(d => d.count))
).sort((a, b) => a - b),
domain: [0, government_programmes_maxCountRE]
},
facet: { data: government_programmesCounts, x: "measure", label: "" },
marginLeft: 60,
style: {
width: 1600,
height: 500,
fontSize: 40,
},
});
Incentivise participation in programmes?
incentivesCounts = aq.from(surveyResults)
.select("incentives")
.groupby("incentives")
.count()
.derive({ measure: d => "" })
// Calculate the maximum count from your dataset
incentives_maxCountRE = Math.max(...incentivesCounts.objects().map(d => d.count));
plot_incentives = Plot.plot({
marks: [
Plot.barY(incentivesCounts, {
x: "incentives",
y: "count",
fill: "incentives",
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(incentivesCounts.objects().map(d => d.count))
).sort((a, b) => a - b),
domain: [0, incentives_maxCountRE]
},
facet: { data: incentivesCounts, x: "measure", label: "" },
marginLeft: 60,
style: {
width: 1600,
height: 500,
fontSize: 40,
},
});
Emphasise criminal or treatment responses?
emphasisCounts = aq.from(surveyResults)
.select("emphasis")
.groupby("emphasis")
.count()
.derive({ measure: d => "" })
// Calculate the maximum count from your dataset
emphasis_maxCountRE = Math.max(...emphasisCounts.objects().map(d => d.count));
plot_emphasis = Plot.plot({
marks: [
Plot.barY(emphasisCounts, {
x: "emphasis",
y: "count",
fill: "emphasis",
stroke: "black",
strokeWidth: 1
}),
Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
],
color: {
domain: [
"criminal",
"treatment"
],
range: [
"darkorange",
"cadetblue"
]
},
marginBottom: 80,
x: { label: "", tickSize: 2, tickRotate: -1, padding: 0.2,
domain: ["criminal", "treatment"]
},
y: {
label: "",
tickSize: 10,
tickFormat: d => d,
tickValues: Array.from(
new Set(emphasisCounts.objects().map(d => d.count))
).sort((a, b) => a - b),
domain: [0, emphasis_maxCountRE]
},
facet: { data: emphasisCounts, x: "measure", label: "" },
marginLeft: 60,
style: {
width: 1600,
height: 500,
fontSize: 40,
},
});
RQs (p834):
What limits the larger pool of extremists who embrace an ideology but do not translate these beliefs into action? What types of conditions serve as barriers in the action pathway process? And, finally, how can the identification of these barriers help inform counterterrorism measures?
RQs (p834):
What limits the larger pool of extremists who embrace an ideology but do not translate these beliefs into action? What types of conditions serve as barriers in the action pathway process? And, finally, how can the identification of these barriers help inform counterterrorism measures?
Simi and Windisch (2020, 833–34):
our sample represents an important step forward in terms of focusing on “negative cases.”
(for methods nerds, further on ‘negative cases’: Emigh 1997; Varshney 2001; Mahoney and Goertz 2004)
p.839 (preference toward interpersonal violence):
They described interpersonal violence as a masculine endeavor, whereas, shooting or bombing people from a distance was considered dishonorable and unfair. As the following participants explained, compared to street fighting, using a gun expresses a lack of masculinity and physical prowess.
p.841 (changes in focus/availability):
The presence of personal obligations can be thought of as changes in “biographical availability” such as employment, marriage, and children. In all, thirteen participants (37 per cent) were identified as experiencing a change in focus and availability, which constrained the likelihood of MCV and shifted their attention toward personal obligations (e.g., children, work).
p.842 (internal organizational conflict):
A common reason for entering extremism is the appeal of joining a higher moral cause predicated on virtues such as loyalty, kinship, and purity.
Rune Ellefsen & Sveinung Sandberg (2022): Everyday Prevention of Radicalization: The Impacts of Family, Peer, and Police Intervention, Studies in Conflict & Terrorism.
Rune Ellefsen & Sveinung Sandberg (2022): Everyday Prevention of Radicalization: The Impacts of Family, Peer, and Police Intervention, Studies in Conflict & Terrorism.
Rune Ellefsen & Sveinung Sandberg (2022): Everyday Prevention of Radicalization: The Impacts of Family, Peer, and Police Intervention, Studies in Conflict & Terrorism.
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Alternatively, send me an email: m.zeller@lmu.de