Presentation groups

Approaches to studying the far right

• selection of topic: theory-driven, method-driven, problem-/empirics-driven
• purpose: describing, explaining, predicting
• use of theory: testing (e.g., puzzles, Day and Koivu 2019) or generating
• concepts: how much is constructed (i.e., new conceptualisation)?
• methods:
  • ontological assumptions: probabilism, determinism, constructivism
  • methodological alignment (Hall 2003)
    • choice of cases: sampling or casing (Soss 2018)?
    • analysis: by variables (generalisation) or by cases (contextualisation)?

conceptualising ‘populism’ (Meijers and Zaslove 2021)

four criteria: (i) construct validity, (ii) multi-dimensionality, (iii) measurement precision and (iv) coverage

• construct validity: capture all relevant components
  • 5 for populism: (1) sovereignty of ‘the people’, (2) ‘the people’ are homogenous, (3) ‘the people’s’ interests are united by a general will, (4) elite portrayed as corrupt, and (5) conflict of ‘the people’ and elite as a moral struggle between good and bad

conceptualisation (Adcock and Collier 2001)

books on researching the far right

Research findings and dissemination

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

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

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

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

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

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

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

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

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

Consequences of research

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

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

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

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

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

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

Common ethics approval points

1. Will individuals participate in this study who belong to a vulnerable group … or who cannot give their own consent to participate (e.g., under the age of 16)?

2. Will it be necessary that people participate in this study without having been informed about this previously or without having given their consent to participate (e.g., as in covert observation)?

3. Will the study involve covert observation or any other method that precludes informed consent, full debriefing, or the opportunity for participants to have their data deleted?

Common ethics approval points

4. Will the study feature questions about topics that are of an intimate nature or that participants may perceive as stigmatising (e.g., questions pertaining to illegal or deviant behaviour or to sexual preferences)?

5. Will video or audio recordings be taken without prior consent by the participants?

6. Which personal data, if any, will be collected? … How will the anonymisation or pseudonymisation of collected data be ensured? … How long will the data be kept and where will they be stored?

What not to do … research design

What not to do … research design

What not to do … reporting

• Shoshan (2016) provides a study of far-right youth in Köpenick district of Berlin
  • author is of Jewish ancestry, so adopting a pseudonym was necessary for research to proceed
  • author was embedded with ‘street social workers’, effectively masquerading as one of them
    • no informed consent, youth participants, covert observation, possibly illegal activity

Kathy Blee’s books

Blee (2007) on ethnography of far right

• many studies of far right offer the view from outside: economic, social, attitudinal, or cultural environments that nurture organised racism and right-wing extremism—not internal dynamics
  • Limitations of such externalist accounts: struggles to explain movement/party emergence and participation
• Ethnography offers solutions
  • common objects of research: political rituals, nature and intensity of FR leaders and groups, and how individual respond
  • includes techniques like life-history interviews with activists, focus groups, observation and/or participation at far-right meetings and events
  • strong in drawing attention researcher positionality problems

Blee (2007) on ethnography of FR - challenges

• mutual mistrust:
  • researchers sometimes fail to scrutinise motives or actions by sympathetic movements/activists—the far right, conversely, is usually regarded with (excessive) scepticism
    • few researchers want to develop rapport (often integral to ethnography) with FR subjects
  • far right often regard academics as untrustworthy or hostile, as “potential agents of an enemy state (police, infiltrators, informants), spies from rival groups, or general antagonists who will expose their operations and cause harm”
    • FR individuals sometimes intimidate or threaten researchers
• This circular mutual mistrust fuels the problem of access

Blee (2007) on ethnography of FR - opportunities

• ethnography has mostly studied progressive movements and other groups with whom researchers sympathise
• close-up, ethnographic (and related) approaches can identify beliefs and motivations of FR activists and supporters
  • for example, emotions: ‘hostility, persecution, and anger certainly are generated and reinforced in the far right, but so are feelings of pride, amusement, and sensuality’. And there is considerable variations between movements.
• sometimes, under certain conditions and with strict rules, researchers may covertly or discretely study FR

Blee (1998) on emotions in fieldwork with the far right

• Fieldwork is emotional: usually both positive and negative emotions are affecting researchers
  • challenging to recognise → demanding committed and discerning introspection
  • unrecognised, can create serious problems of bias (and others) for research work
  • plus, personal consequences for researcher well-being
• Emotions in fieldwork are negotiated and relational (not individual)

Blee (1998) on emotions in fieldwork with the far right

• YET emotional dimensions of fieldwork can be useful:
  1. emotional dynamics between respondent and researcher can be analysed,
  2. researcher’s emotions are good data in themselves, so keep an ‘emotional log’ (what is respondent trying to evoke? sympathy, understanding, fear, aversion to transgressive/provocative statements? how is the interviewer responding?)
• empathy, rapport, and trust with sympathetic respondents; (mutual) fear with ‘unloved groups’
  • “My fear of being harmed and the respondents’ fears of being exposed…”

Background of Blee’s studies

• research aim: the role of women within active self-defined racist and antisemitic groups operating in the U.S. in the 1994-1995.
• data:
  • unstructured life history interviews
  • structured questionnaires
  • 34 women who were active members of a racist group
  • plus, an analysis of propaganda published by more than 100 then active racist and antisemitic groups.

Blee’s article (and other output from this project) is a masterful display of research transparency – take inspiration for your thesis!

Methods point: Blee’s justification for life histories

• Blee (1998, 387) states three reasons
  1. avoid the tendency of racist activists to substitute organisational doctrine for personal belief in standard interview settings (as in news interviews)
     • differentiating between individuals and groups, avoiding skewed/misleading data
  2. wanted to get narratives for causation of respondents’ personal development
     • method to suit research aim
  3. life histories and narratives, rather than on the beliefs, to minimize the potential for this study to be used to broadcast racist ideologies to new audiences

Blee (1998) - finding respondents

• locating interview respondents:
  • snowball sampling not possible: though common in studies of ‘hidden communities,’ practically, “animosity among racist groups meant that members could not be relied upon to suggest respondents from other groups” (recognition of internal dynamics of study population)

Blee (1998) - introductory interview communication

I made it clear that I did not share the racial convictions of these groups. I explicitly said that my ideological views were quite opposed to theirs, that they should not have any hope of converting me to their views, but that I would try to present an accurate depiction of women racist activists. This stance—as distant but not neutral researcher—was intended to clarify the nature of my interest in racist activists and their movement. It also positioned me as an observer who had not decided in advance to depict them as crazy or as personally pathological (a common media portrayal) and thus increase their vulnerability to incarceration by law enforcement or mental health agencies.

Blee (1998) - introductory interview communication

I made it clear that I did not share the racial convictions of these groups. I explicitly said that my ideological views were quite opposed to theirs, that they should not have any hope of converting me to their views, but that I would try to present an accurate depiction of women racist activists. This stance—as distant but not neutral researcher—was intended to clarify the nature of my interest in racist activists and their movement. It also positioned me as an observer who had not decided in advance to depict them as crazy or as personally pathological (a common media portrayal) and thus increase their vulnerability to incarceration by law enforcement or mental health agencies.

• explains positionality, interest, and intent (no whiff of deception)
• doing no harm
• plus, openness during interviews to explain position further, even if respondents were uninterested (Blee 1998, 386)

Blee (1998) - arranging interview, negotiating setting

• asked respondents: choose a comfortable place but without likelihood of interruption
  • refused on two occasions (for safety reasons): (1) get blindfolded and transported to unknown destination in back of a truck, (2) meeting in a remote racist compound, driven by a racist group member

Blee (1998) - project duration developments

• fear abated, numbness to extreme ideas set in
• in one interview, ‘seductive, false rapport,’ as Blee (1998, 393) describes

Actually with Linda and [her boyfriend] there was no indication that they might try to harm me at all. In fact, quite the contrary. I actually was afraid of that before they came because they both have very violent reputations, but in person they were extremely cordial and very friendly, not trying to intimidate me in any way. Perhaps trying to cultivate me.

Blee (1998) - interview dynamics, research effects

• respondents frequently pointed out researcher’s vulnerability (fear and intimidation)
• acknowledge that persons from disagreeable groups can be quite agreeable respondents (emotional dissonance for researchers)