Class 9: Aspects of Social Movement Activity

State responses

Opening notes

Presentation groups

Presentations line-up
Date	Presenters	Method
4 Dec:	Daichi, Seongyeon, Jehyun	ethnography
18 Dec:	Ayla, Tara, Theresa, Annabelle	TBD
15 Jan:	Luna, Emilene, Raffa, Sofia	TBD

State responses

opening questions
overview
one policy change example
repression/social control
DE state responses to climate movement
- FFF and LG

Alf Excellent Response GIFfrom Alf GIFs

Opening questions

How (in what different ways) can state actors respond to movements?

Who/which state actor is taking action? What kind of action is it? How visible is it?

State responses overview

ignore / dismiss
oppose
accommodate

State responses overview

ignore / dismiss
oppose
- close opportunities (‘problem depletion’)
- ‘channel’, restrict access to resources
- repress, apply force
accommodate
- encourage institutionalisation
- engage in relevant decision-making processes
- change policy

Movements and policy change (Jones 2022)

anyone know what this place is?

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.”

\(\leftarrow\)
Orban government’s Memorial to the Victims of the German Occupation
\(\leftarrow\)

\(\rightarrow\)
Counter-monument by independent historical memory activists
\(\rightarrow\)

3 dimensions of repression/social control (Earl 2003)


Identity of repressive agent	State agents tightly connected with national political elites (e.g., military units)	State agents loosely connected with national political elites (e.g., local police departments)	Private agents (e.g., counter-demonstrators)
Character of repressive action	Coercion (e.g., use of tear gas and rubber bullets)	Channelling (e.g., restrictions on registered organisations)
Whether repressive action is observable	Observable (i.e., overt; e.g., Tiananmen Square)	Unobserved (i.e., covert or latent; e.g., COINTELPRO)

What sort of repression/social control in cases do you know of? Was it effective? Why/How?

DE climate movement - FFF

March 2019: Chancellor Merkel praises FFF (link)
April 2019: government minister (for Economy and Energy, Peter Altmeier) requests to speak at FFF demo (rejected by organisers) (link)
August 2020: Chancellor Merkel meets with FFF representatives (link)
March 2021: constitutional court (Bundesverfassungsgericht) declares federal climate protection law (Bundes-Klimaschutzgesetz) insufficient, requiring more goverment action
influence on United Nations Climate Change conferences, COPs 25-28

DE climate movement - Letzte Generation

2022: Green party (potential elite allies) distance themselves from LG
December 2022/May 2023: police searches of LG properties, related to prosecutions (link and link)

April 2023: violent police tactics to remove blockaders (link)
- ‘pain grips’ (Schmerzgriffe): wristlocks and other ‘control/restraint holds’
use of preventive detention in Bayern (link) and calls for it elsewhere by police union chief (link)
politicians, some from governing parties, refering to ‘climate terrorists’ (Klimaterroristen) and ‘climate RAF’ (Klima-RAF)

Poll: State responses

A QR code for the survey.

Take the survey at https://forms.gle/jkBAGEAx5Ub9LeoT6

good when state agrees to protest demands?
non-violent protest inviolable?
state response to movement disruption of services?

is state surveillance of a movement ever acceptable?
what determines whether movement treated as ‘threatening’?
what typically happens after a state violently represses a movement?
states respond more harshly to ‘marginalised group’ movements?

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-1vT3CB5F0YxqtGGIZfOEhdhime5_ChcJ9Uau4s0OETYdrpHOy25pqGt5cNyGc9Ig51LmkWjBfHDzfZB6/" +
    "pub?gid=1350101088&single=true&output=csv",
  10000, 1, 8); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

good when state agrees to protest demands?

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

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

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

determines whether movement treated as ‘threatening’?

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

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

plot_determines = Plot.plot({
  marks: [
    Plot.barY(determinesCounts, {
      x: "determines",
      y: "count",
      fill: "determines",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "movement tactics", 
      "movement ideology", 
      "movement participants", 
      "framing in the media",
      "other"
    ],
    range: [
      "goldenrod",
      "cadetblue",
      "darkorange",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 400,
  x: { label: "", tickSize: 2, tickRotate: -40, padding: 0.2,
    domain: ["movement tactics", "movement ideology", "movement participants", "framing in the media", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(determinesCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, determines_maxCountRE]
  },
  facet: { data: determinesCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Poll - harder state responses

state response to movement disruption of services?

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

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

plot_disrupts = Plot.plot({
  marks: [
    Plot.barY(disruptsCounts, {
      x: "disrupts",
      y: "count",
      fill: "disrupts",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "remove protest", 
      "remove and arrest", 
      "tolerate", 
      "negotiate", 
      "support",
      "other"
    ],
    range: [
      "red",
      "orange",
      "yellow",
      "green",
      "blue",
      "indigo"
    ]
  },
  marginBottom: 400,
  x: { label: "", tickSize: 2, tickRotate: -60, padding: 0.2,
    domain: ["remove protest", "remove and arrest", "tolerate", "negotiate", "support", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(disruptsCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, disrupts_maxCountRE]
  },
  facet: { data: disruptsCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

state surveillance of a movement ever acceptable?

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

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

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

If yes, when?

what after a state violently represses a movement?

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

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

plot_repress = Plot.plot({
  marks: [
    Plot.barY(repressCounts, {
      x: "repress",
      y: "count",
      fill: "repress",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "movement collapses", 
      "movement radicalises", 
      "support for movement grows", 
      "movement goes underground",
      "other"
    ],
    range: [
      "black",
      "red",
      "forestgreen",
      "navy",
      "gray"
    ]
  },
  marginBottom: 400,
  x: { label: "", tickSize: 2, tickRotate: -70, padding: 0.2,
    domain: ["movement collapses", "movement radicalises", "support for movement grows", "movement goes underground", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(repressCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, repress_maxCountRE]
  },
  facet: { data: repressCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Poll - motivated state responses

states respond more harshly to ‘marginalised group’ movements?

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

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

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

non-violent protest inviolable?

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

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

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

Even if protest is anti-democratic/ hateful/discriminatory?

Research closer look

Berntzen and Weisskircher (2016) - mobilisation differences and state responses
- PEGIDA background
- research questions and design
- cases and data collection
- findings
- discussion question

Closer Look GIFfrom Closer GIFs

PEGIDA background: movement framing

PEGIDA as a ‘serious civil rights movement’? (H.C. Strache)

PEGIDA background: a demonstration in Wien

Wien, 2 February 2015

Berntzen and Weisskircher (2016) - RQ

why did PEGIDA mobilize to some extent in Austria and Norway, while failing in Sweden and Switzerland?

case selection: two countries from economically and culturally similar regions (German-speaking Central Europe [Switzerland and Austria] and Scandinavia [Norway and Sweden]), each with variation on the ‘dependent variable’ of street mobilisation

sort of paired ‘most similar systems design’ (MSSD)
key IVs: state bans, parliamentary strength of the radical right, counter-mobilisation by anti-racist groups
- why these IVs? are there other important factors? what are the hypothesised effects?

Berntzen and Weisskircher (2016) - cases

thickness of grey stream shows aggregate online activity by PEGIDA groups. Circles indicate protesters in month, number inside circle shows demos per month

Austria: significant mobilisation

Switzerland: insignificant mobilisation

Norway: significant mobilisation

Sweden: insignificant mobilisation

Berntzen and Weisskircher (2016) - data collection

gaps in this data collection? anything other data needed?

PEGIDA Facebook memberships

Berntzen and Weisskircher (2016) - findings

supports negative impact of established radical right parties on street mobilisation
no strong finding on counter-mobilisation:

“it is possible that the massive level of resistance has curtailed PEGIDA to a certain extent by making it costlier for people to march under their banner. Nevertheless, this cannot explain cross-national variation as anti-racists mobilised strongly in all four countries”

Berntzen and Weisskircher (2016) - network

Austrian group: largest, most popular (ties to 11 other PEGIDA groups)
Norwegian group: low relevance in the wider community
Swiss group: many national ties, few external ties
Swedish group: few connections

Berntzen and Weisskircher (2016) - findings

Attempts to mobilise and spread propaganda online by the transnational radical right are therefore vulnerable to police and state bans, especially if they are put in place at an early stage in their mobilisation efforts. This lays bare the potential for curtailing far-right activism in multiple arenas by targeting and denying them the opportunity of rallying and getting attention through street activism.

Berntzen and Weisskircher (2016) - findings

Sweden PEGIDA: restrictively policed due to high counter-mobilisation
- new mobilisation: no strong benefit of pre-existing radical right activism; in all other cases, PEGIDA label was used by established radical right actors
Switzerland PEGIDA: protests banned by local authorities
- banning demonstrations (offline) also negatively impacted online activism
Norway PEGIDA: outgrowth of pre-existing radical right activism
- bans on demonstrations in Oslo—also negatively impacted online activism
Austria PEGIDA: deeply embedded in national and transnational far-right scene, some support from FPÖ (large, radical right political party)
- establishing stable online presence reshapes (expands) online far-right scene

counter-mobilisation and radical right party strength do not explain differences in mobilisation—state action (esp. banning) does
- are you convinced by this?

Berntzen and Weisskircher (2016) - discussion question

All PEGIDA events in Austria were met with much greater countermobilisation. Some PEGIDA activists were reported to the police because of engagement in National Socialist activities, such as Hitler salutes.

Is counter-mobilisation needed to provoke a state response?

some random researcher strongly contending ‘yes’: Zeller (2021), Zeller (2022), Zeller and Vaughan (2024), Zeller (2025)

to be continued with next session, on ‘counter-mobilisation’

Any questions, concerns, feedback for this class?

Anonymous feedback here: https://forms.gle/AjHt6fcnwZxkSg4X8

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

References

Berntzen, Lars Erik, and Manès Weisskircher. 2016. “Anti-Islamic PEGIDA Beyond Germany: Explaining Differences in Mobilisation.” Journal of Intercultural Studies 37 (6): 556–73. https://doi.org/10.1080/07256868.2016.1235021.

Earl, Jennifer. 2003. “Tanks, Tear Gas, and Taxes : Toward a Theory of Movement Repression.” Sociological Theory 21 (1): 44–68.

Jones, Sam. 2022. “Spanish Fascist’s Family to Exhume Remains from Valley of the Fallen.” The Guardian, October, 2.

Zeller, Michael C. 2021. “Patterns of Demobilization: A Qualitative Comparative Analysis (QCA) of Far-Right Demonstration Campaigns.” Mobilization: An International Quarterly 26 (3): 267–84. https://doi.org/10.17813/1086-671X-26-3-267.

———. 2022. “Demobilising Far-Right Demonstration Campaigns: Coercive Counter-Mobilisation, State Social Control, and the Demobilisation of the Hess Gedenkmarsch Campaign.” Social Movement Studies 21 (3): 372–90. https://doi.org/10.1080/14742837.2021.1889493.

———. 2025. “Pragmatic Rather Than Principled: Organisational Bans in Democracies.” European Journal of Political Research 0 (0): 1–30. https://doi.org/10.1017/S1475676525100121y.

Zeller, Michael C, and Michael Vaughan. 2024. “Proscribing Right-Wing Extremist Organizations in Europe: Variations, Trends, and Prospects.” Terrorism and Political Violence 36 (8): 985–1007. https://doi.org/10.1080/09546553.2023.2240446.