Presentation groups

Klausur essay question example

What sort of impacts can social movements have? Discuss with examples

Theories of social movements, conceptual origins

Theories of social movements, conceptual origins

• Collective behaviour theory: why is movement activity happening?
  • movements as consequences and manifestations of strain, deprivation, and grievance
• Resource mobilisation theory: how is movement activity happening?
  • focus on organisations: how they mobilise and campaign in strategic pursuit of goals
  • types of resources, including: material, human, organisational, moral
• Political process theory: what makes/shapes movement activity?
  • movements are products of the political environment in which they emerge, responding to socio-political changes (opportunity/threat) and being met with (broadly) facilitation or repression or disregard

Key concepts (1)

• opportunities (Tarrow 2011, 32): “consistent – but not necessarily formal, permanent, or national – sets of clues that encourage people to engage in contentious politics.”

• political opportunity structure (Kitschelt 1986, 58) - “are comprised of specific configurations of resources, institutional arrangements and historical precedents for social mobilisation, which facilitate the development of protest movements in some instances and constrain them in others”

  • concept formation of POS should be specific to a given movement

3 POS effects on movements

1. What resources (‘coercive, normative, remunerative and informational’) can an emergent movement draw upon?
2. How can movements access the public sphere and political decision-making? (what laws regulate such access)
3. Are there other movements that model (and ease) mobilisation and movement emergence?

Key concepts (2)

discursive opportunity structure (Koopmans and Olzak 2004, 202–5): aspects of the public discourse that determine a message’s chances of diffusion in the public sphere

Key concepts (3)

• framing (‘ideology’) - the meanings individuals or groups attach to events, developments, activities, and other individuals/groups
  • types of frames: diagnostic, prognostic, motivational;
    • injustice frames - When problems are attributed to individuals’ or groups’ ignorance, indifference, or malice, the result is a sense of injustice
  • master frame: an overarching frame that smaller/sub-issue frames fit into
  • counter frame: a frame opposed to another group’s frame (framing contest)

Key concepts (4)

• frame bridging - linking of two or more ideologically congruent but structurally unconnected frames regarding a particular issue or problem

• frame amplification - idealization, embellishment, clarification, or invigoration of existing values or beliefs

• frame extension - depicting an SMO’s interests and frame(s) as extending beyond its primary interests to include issues and concerns that are presumed to be of importance to potential adherents

• frame transformation - changing old understandings and meanings and/or generating new ones

• credibility? salience?

Key concepts (6)

Key concepts (7)

• strategy - a combination of a claim (or demand), a tactic, and a site (or venue); alternatively, consisting of 3 elements:
  1. Targeting - who/what is being acted upon by tactics
  2. Tactics - types of collective action and manner of their performance
  3. Timing - some moments present greater opportunity than others
  • Remember: strategy is a product of (rational) choice, BUT also a part of collective identity involving moral and emotional committments
  • demonstration vs. direct action
    • Demonstrations inconvenience or embarrass authorities and establish the movement’s social support but never themselves attain the collective goal
    • Direct action seize resources to satisfy their demands or take action to resolve a grievance; seeks itself to achieve collective goals

Key concepts (8)

• Gamson (1990) found that violent social movements (incl. ‘strikes and disruptive techniques’) are more likely than nonviolent to achieve their goal
  • more effective in attracting attention and imposing costs on targets/opponents - similarly found by Cress and Snow (2000)
• Chenoweth and Stephan (2011): non-violent more than twice as likely to achieve full or partial success compared to violent cases
  • nonviolent campaigns elicit broad and diverse support, create more opposition defections, have more tactical options, often maintain discipline

Key concepts (9)

• social control hypothesis (e.g., Piven and Cloward 1979)
  • disruptive protests/tactics allow movements to win concessions in exchange for ending protests/tactics (coercion mechanism)
• mass mobilisation/social pressure hypothesis (e.g., Chenoweth and Stephan 2011)
  • gaining enough (visible) support to pressure decision-makers into concessions (consensus/demonstrative/persuasion mechanisms)
    • implicit appeal to democratic norms
• radical flank effects - dynamics between (relatively) moderate and (relatively) radical parts of a movement and their target(s)
  • positive effect - compels target/authorities to accept moderate demands
  • negative effect - spooks potential elite allies

Key concepts (10)

• McAdam (1983): tactical interaction
  • movements disrupt as they mount a challenge
  • authorities/targets adapt to tactics, dulling their impact
  • movements innovate tactics to maintain effective strategy
  • this cycle places high demands on movements

Key concepts (11)

• coalitions - may be enduring, may be short-term
  • factors critical to coalition formation: social ties, conducive org. structures, ideology/culture/identity, institutional environment, resouces
  • help build/enhance networks, increase resource availability, expand tactical repertoires
• media
  • plays gatekeeper role
  • factors affective media coverage: day of week, other events, weather, celebrities, size/violence/counterdemo of event
• astroturfing - manipulation of public sphere (media, information, movement scene)

Key concepts (12)

• party responses to movements: dismissive, accommodative, adversarial/oppose
• state responses to movements: ignore/dismiss, oppose (close opportunities, channel, repress), accommodate (encourage institutionalisation, engage in policymaking processes, change policy)

Key concepts (13)

• dimensions of repression:

Key concepts (14)

• countermovement (‘opposing movements’) - a movement that makes contrary claims simultaneously to those of the original movement
  • involve sustained counter-mobilisation
  • characterised by polarisation, dependency, Manicheism, imitation
  • more likely to appear when… (a) originating movement shows signs of success, (b) that success includes threats to existing interests, (c) (elite) allies are available to support counter-mobilisation
  • divided governments/authorities are more likely to provoke movement-countermovement contention because they cannot decisively ‘close’ issues
  • federal systems are more likely to sustain movement-countermovement contention because there are venues/arenas
  • movements and countermovements form part of the opportunity structure of each other
• critical events, for movements, can be government or state actions, accidents/incidents, large or conspicuous demonstrations

Key concepts (15)

• internet tools allow movements…
  • (+) greater speed (and accuracy) of communication, less cost of communication, greater connectivity across distances
  • (-) to scale up without building strong organisation
• movement impact
  • on individuals, on organisations, on cultures, on politics
    • positive, negative (backlash), null impacts

Tips for preparing for Klausur

• review class slides

• reread your notes from readings

  • maybe (re-)read a couple of the required readings

• think through cases you know of

• think through other cases we discussed (through readings or your peers’ expertise)

• don’t panic

Demobilisation and disengagement defined

Demobilisation (Zeller forthcoming; cf. Davenport 2015)

the process whereby collective action, whether in the form of campaigns, organisations, or even whole movements, decreases and ultimately ends

Factors influencing demobilisation

Factors influencing demobilisation

causes overlap and interact in different demobilisation processes

Factors influencing demobilisation

applicable to campaigns, organisations, whole movement sectors

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-1vQB00gFW5PK3umIX_FBaWj_WrSsHXqIjv-U94Qmuez5vcGupISgymKVq2Z61BWofmyFrRXPuUvMuHZN/" +
    "pub?gid=624052779&single=true&output=csv",
  10000, 1, 6); // adjust the last number to select all relevant columns 

respondentCount = surveyResults.length;

What should activists / movements do if their demands are met, goals achieved?

MetDemands = aq.from(surveyResults)
  .select("demands_met")
  .filter(d => d.demands_met) // to exclude empty ("") strings, 0s, FALSEs, nulls, etc

MetDemands.array("demands_met")

Poll results

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

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

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

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

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

plot_common_factor = Plot.plot({
  marks: [
    Plot.barY(common_factorCounts, {
      x: "common_factor",
      y: "count",
      fill: "common_factor",
      stroke: "black",
      strokeWidth: 1
    }),
    Plot.ruleY([respondentCount], { stroke: "#ffffff99" })
  ],
  color: {
    domain: [
      "weak leadership", 
      "movement splits", 
      "opposition from elites", 
      "loss of public support",
      "other"
    ],
    range: [
      "goldenrod",
      "cadetblue",
      "darkorange",
      "forestgreen",
      "violet"
    ]
  },
  marginBottom: 400,
  x: { label: "", tickSize: 2, tickRotate: -40, padding: 0.2,
    domain: ["weak leadership", "movement splits", "opposition from elites", "loss of public support", "other"]
  },  
  y: {
    label: "",
    tickSize: 10,
    tickFormat: d => d,
    tickValues: Array.from(
      new Set(common_factorCounts.objects().map(d => d.count))
    ).sort((a, b) => a - b),
    domain: [0, common_factor_maxCountRE]
  },
  facet: { data: common_factorCounts, x: "measure", label: "" },
  marginLeft: 60,
  style: {
    width: 1600,
    height: 500,
    fontSize: 40,
  },
});

Poll results - institutionalisation

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

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

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

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

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

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

Social movement campaigns

• Campaigns consist of four elements (della Porta and Rucht 2002; Staggenborg and Lecomte 2009)
  1. a consistent organising actor (an activist[s], an organisation[s]),
  2. temporal boundedness,
  3. strategically linked actions, and
  4. the intention to advance goals

Far-right demonstration campaigns (AT, DE, UK)

Far-right demonstration campaigns (Zeller forthcoming)

Far-right demonstration campaigns (Zeller forthcoming)

• concentrated state channelling (SCH)

Far-right demonstration campaigns (Zeller forthcoming)

• concentrated state coercion (SCO)
• concentrated state channelling (SCH)

Far-right demonstration campaigns (Zeller forthcoming)

• concentrated private channelling (PCH)
• concentrated state coercion (SCO)
• concentrated state channelling (SCH)

Far-right demonstration campaigns (Zeller forthcoming)

• concentrated private coercion (PCO)
• concentrated private channelling (PCH)
• concentrated state coercion (SCO)
• concentrated state channelling (SCH)

Far-right demonstration campaigns (Zeller forthcoming)

• long duration (LD) (on temporality, see Grzymala-Busse 2011)
• concentrated private coercion (PCO)
• concentrated private channelling (PCH)
• concentrated state coercion (SCO)
• concentrated state channelling (SCH)

Far-right demobilisation patterns (Zeller forthcoming)

FR demobilisation processes (Zeller forthcoming)

Example of the EDL (Morrow and Meadowcroft 2018)

English Defence League (EDL)

• important case: return to street politics, lots of successors (FLA, DFLA, Free Tommy Robinson, Justice for Women and Children)

Example of the EDL (Morrow and Meadowcroft 2018)

rejection of pluralism and minority rights means [EDL] can properly be considered ‘far right’ (p545) (cf. Mudde 2019)

EDL (Morrow and Meadowcroft 2018)

%%flowchart LR
%%  A["`EDL 
%%  campaign
%%  kjaasdafjkjagk`"]:::greenBox --> B[reactive<br/>policing]:::yellowBox
%%  A --> C[counter-<br/>protest]:::yellowBox 
%%  B --> D[public<br/>disorder,<br/>arrests]
%%  C --> D
%%  D --> E[preventative<br/>policing]:::yellowBox
%%  E --> F[EDL<br/>benefits<br/>reduced]

flowchart LR
  A[EDL<br/>campaign:<br/>violence,<br/>increased<br/>self-worth,<br/>solidarity]:::greenBox --> B[heavy,<br/>reactive<br/>policing]:::yellowBox
  A --> C[counter-<br/>demonstration<br/>activity]:::yellowBox 
  B --> D[public disorder<br/>and numerous<br/>arrests]
  C --> D
  D --> E[policing<br/>adaptation:<br/>preventative]:::yellowBox
  E --> F[benefits to<br/>members esp.<br/>violence and<br/>group solidarity<br/>reduced]


  %% Style definitions - classDef blueBox fill:#d0e6ff,stroke:#003366,stroke-width:2px;
  %% classDef greenBox fill:#d4edda,height:150,width:160;
  %% classDef yellowBox fill:#fff3cd,height:100,width:70;
  classDef blueBox fill:#d0e6ff;
  classDef greenBox fill:#d4edda;
  classDef greyBox fill:#e2e3e5;
  classDef redBox fill:#f8d7da;
  classDef yellowBox fill:#fff3cd;

From 2012, the police more effectively kept EDL and counter-demonstrations apart, and routed protests away from potential flashpoints such as Islamic centres