Youth Citizenship, Civic Engagement & Research Methods
Stella Hart — Youth, Citizenship & Community
Stella Hart – “The ‘Problem’ with Youth: Young People, Citizenship and the Community”
Main Argument
Main Argument: New Labour’s model of citizenship is normative, disciplining youth into idealized citizens by linking rights to responsibilities. This excludes youth as active citizens and promotes alienation.
Key Concepts
- Normative Citizenship
- Focused on duties (volunteering, parenting, law-abiding behaviour).
- Enforced via policies like ASBOs, curfews, and Citizenship Education.
- Youth are positioned as “not-yet-citizens” (Lister).
- Cultural Citizenship
- Advocates for inclusion, mutual respect, and recognition of differences.
- Treats youth as “differently equal” citizens with voice and agency.
- Citizenship defined relationally, not through rigid behavioural standards.
Findings from Nottingham Study
- Youth feel disrespected by adults, police, and institutions.
- Fear and marginalization dominate experiences in communities.
- Despite this, they seek inclusion, value community care, and wish for recognition.
- Participation in civic life is low due to the belief that they won’t be listened to.
Takeaway
Citizenship must move beyond behavioral norms to embrace youth voices, lived experiences, and mutual respect. The state’s failure to do this alienates youth and undermines democratic engagement.
Leonard Schoppa — Residential Mobility & Civic Life
Leonard Schoppa – “Residential Mobility and Local Civic Engagement in Japan and the U.S.”
Research Focus
Research Focus: Explains why local civic engagement is higher in Japan than in the U.S., especially in contexts like children’s walk-to-school safety.
Main Argument
Main Argument: Low residential mobility in Japan forces citizens to stay and invest in their communities, whereas in the U.S., high mobility leads to individualistic, exit-based responses (e.g., driving kids to school).
Exit vs. Voice Framework (Hirschman)
- Exit (U.S.): Move away from problems (suburbanization, privatized solutions).
- Voice (Japan): Engage collectively (PTA patrols, local planning).
Key Differences
- Japan: Strong neighbourhood associations, PTA participation, volunteer patrols.
- U.S.: Strong national organizations (NRA, Sierra Club), but weak local ties.
Conclusion
Local civic engagement is structurally conditioned by housing markets and mobility, not just cultural norms. Civic participation requires institutional incentives to invest locally.
POLC78 Final Exam — Cheat Sheet (Side 2)
CHEAT SHEET – POLC78 FINAL EXAM (SIDE 2)
Clément, Lindemann & Sangar — The Hero-Protector Narrative
Clément, Lindemann & Sangar – “The Hero-Protector Narrative”
Core Idea
Core Idea: Political actors use the hero-protector narrative to justify violence through emotional appeals: compassion, fear, and moral anger.
Narrative Structure
- Roles: Hero, Victim, Aggressor, Coward.
- Logic: Inaction = cowardice; action = moral duty.
- Appeals to masculine protection logic across cultures.
Empirical Findings
- Both Bush and Bin Laden used this framework:
- Bush: Liberation and democracy.
- Bin Laden: Defense of Islam and retaliation.
- Despite ideological opposition, both leveraged similar emotional justifications.
Takeaway
Recognizing these discursive structures helps us understand how emotional legitimacy is created to rationalize violence.
Group Consciousness & Afro-Caribbean Interviews
Group Consciousness & Afro-Caribbean Interviews
Core Idea
Core Idea: Explores how Black immigrants (Afro-Caribbeans) understand race and political identity through qualitative interviews.
Key Findings
- Traditional African American group-consciousness surveys often don’t apply to immigrants.
- However, race-consciousness emerges in context, especially regarding policing, discrimination, and injustice.
Takeaway
Qualitative interviews reveal that group identity is fluid and context-specific. Fixed survey measures miss these nuances.
Evaluating Arguments: Friedman vs. Blaydes & Linzer
Purpose: Shows the difference between journalistic vs. scholarly political writing.
Friedman
- Story-driven, metaphorical, emotionally persuasive.
- Lacks data and methodology.
Blaydes & Linzer
- Evidence-based, uses hypotheses and empirical tests.
- Generalizable and falsifiable.
Takeaway
Political science prioritizes rigorous methods and evidence over persuasive rhetoric. Learn to distinguish persuasion vs. validation.
Sides & Gross — Stereotypes and the War on Terror
Sides & Gross – “Stereotypes and the War on Terror”
Main Finding
Main Finding: Support for the War on Terror is driven by stereotypes of Muslims, especially perceptions of low warmth (threatening, untrustworthy), not just ethnocentrism.
Key Insights
- Warmth is a stronger predictor than competence.
- Negative images (e.g., Muslims as violent) justify support for aggressive policy.
- Domestic Muslim-Americans are viewed more favorably than global Muslims.
Takeaway
Understanding specific stereotype dimensions is essential in analyzing political behaviour and public opinion.
Blaydes & Linzer — Anti-Americanism in the Muslim World
Blaydes & Linzer – Anti-Americanism in the Muslim World
Main Argument
Main Argument: Anti-American sentiment in Muslim countries is shaped by media exposure, religiosity, and political context.
Key Findings
- People most hostile to the U.S. are highly religious and consume critical media.
- Political competition and Islamist movements fuel anti-American views.
- Economic hardship is not the main factor.
Methodology
- Multilevel regression using Pew Global Attitudes data.
- Controls for both individual and national-level variables.
Takeaway
Anti-Americanism is politically constructed, not just culturally ingrained. Elite discourse and media shape public attitudes.
1. Associations and Hypothesis Testing
- Association: A relationship between two variables; can be positive or negative.
- Hypothesis Example: Older people are more politically efficacious than younger ones → Data confirms: reject the null hypothesis.
- Correlation: Used to measure association (Pearson’s r for continuous variables).
- Ranges from −1 to +1.
- Do not infer causation from correlation alone.
2. Quantitative Methods
- Quantitative Analysis:
- Involves many observations.
- Used to detect patterns and test hypotheses.
- Beta Coefficient:
- In finance, it measures how much an investment responds to market changes.
3. Regression Analysis
- Linear (OLS) Regression:
- Models continuous outcomes.
- Problems arise when used with dichotomous (binary) dependent variables.
- Logistic Regression (Logit):
- Used when the dependent variable is binary (0/1).
- Predicts the probability of an outcome occurring.
- Curves (logit/probit) fit binary data better than linear lines.
- Output: coefficients (converted into odds ratios or marginal effects).
4. Evaluating Arguments (4 Tests)
- Truthfulness of Premises: Are assumptions factually accurate?
- Logical Strength: If premises are true, does the conclusion logically follow?
- Relevance: Is the premise relevant to the conclusion?
- Non-Circularity: Avoid tautologies and restatements (e.g., “Reagan was a great communicator because he spoke well”).
5. Fallacies
- Appeals to Ignorance: Lack of disproof ≠ proof.
- Appeals to the Mob: “Everyone agrees, so it must be true.”
- Appeals to Emotion: Manipulating feelings instead of logic.
- Ad Hominem: Attacking the person rather than the argument.
- Straw Man: Misrepresenting someone’s argument to make it easier to attack.
6. Lies vs. Bullshit (BS)
- Lying: Intending to deceive, knowing the truth.
- BS (Frankfurt): Speaker doesn’t care if the statement is true or false; only cares about personal agenda.
- BS Detection: Often requires knowledge about the speaker, not just the content.
7. Avoiding BS in Science
- Scientific Method: Hypotheses should be testable and falsifiable.
- Hypothesis Testing: Builds credibility by enabling rejection or refinement of theories.
8. Interviewing & Focus Groups
- Good Practices:
- Begin with simple “what” questions.
- Move to “how” and “why” questions.
- Avoid abrupt topic transitions.
- Focus on Individuals: Ask about personal views and experiences.
- Avoid Overgeneralization: 1–3 people do not represent a broader population.
- Flexibility: Follow up on unexpected but relevant responses.
9. Comparative Research Designs
- Most Similar Systems: Control for many variables, isolate differences.
- Most Different Systems: Identify commonalities despite differences.
- Used in both qualitative and quantitative studies.
10. Reading Quantitative Data
- Descriptive Statistics: Overview of variables (e.g., mean, percentages).
- Regression Tables:
- Significance levels (p-values): Indicate confidence in results.
- Coefficients: Show direction and strength of variable effects.
- Statistical vs. Substantive Significance:
- Statistical: Is the result unlikely to be due to chance?
- Substantive: Is the result meaningful in real-world terms?
11. Scholarly Article Structure
- Abstract: Summary of purpose, method, findings.
- Intro: Relevance and puzzle.
- Literature Review: Existing research, gaps.
- Methods: Design (quantitative/qualitative/mixed).
- Results: Data analysis, regression tables.
- Discussion: Interpretation, limitations.
- Conclusion: Summary, implications, future research.
12. Unpacking Articles (Schoppa, Rodrik, etc.)
- Schoppa: Uses comparative design (Japan vs. US).
- Rodrik: Quantitative regression; significance and model fit emphasized.
- Blaydes & Linzer: Use multiple methods, test multiple hypotheses.
- Friedman vs. Drezner:
- Friedman = opinion-based, possibly BS.
- Drezner critiques lack of evidence and poor logical structure.
- Blaydes & Linzer = scientific, methodological rigor.
13. Final Takeaways
- Be ready to analyze tables, identify fallacies, and compare argument styles.
- Understand how to evaluate qualitative interviews, read quantitative tables, and assess argument credibility.
- Know the differences between academic vs. opinion-based writing.
1. Interviewing & Focus Groups
Differences from Surveys:
- Surveys collect standardized data from large samples; they are structured and quantifiable.
- Qualitative interviews explore depth; they are open-ended, flexible, one-on-one, and can follow up on responses.
- Focus groups involve moderated group discussions to capture interactions, group norms, and shared meanings.
Strengths & Threats to Validity/Reliability:
- Strengths: Provide rich, in-depth data; allow for follow-up; capture context and lived experience.
- Threats: Interviewer bias, limited generalizability, groupthink in focus groups, social desirability bias.
Best Suited Hypotheses:
- Those exploring subjective experiences, meanings, and motivations (e.g., “How do youth perceive respect from authorities?”).
Application: Stella Hart’s study on youth used both interviews and focus groups to uncover marginalized youth perspectives excluded by normative models of citizenship.
2. Comparative & Qualitative Research
Main Points from Three Articles:
- Hart: Youth feel excluded by New Labour’s normative citizenship, which emphasizes duties over rights. Advocates a cultural citizenship that includes youth voices.
- Case Selection: Often based on logic, not representativeness (e.g., selecting schools with diverse class/ethnicity).
- Designs:
- Most Similar Systems Design (MSSD): Cases are similar on many factors but differ on a key variable.
- Most Different Systems Design (MDSD): Cases differ but share the outcome being studied.
Key Concepts:
- Causality: Often probabilistic.
- Validity: Emphasis on depth over breadth.
- Logic of Inference: From observed patterns to broader claims.
3. Interview Practicum – Hart & Rogers
- Hart: Young people were positioned as “not-yet-citizens” (Lister), heavily monitored, and unfairly stereotyped.
- Emphasizes relational and cultural citizenship, recognizing youth as “differently equal” with real experiences of disrespect and exclusion.
- Fieldwork included interviews and focus groups with youth in Nottingham to reveal feelings of marginalization, mistrust in institutions, and desire for community inclusion.
- Key Themes from Interviews: Crime, (dis)respect, lack of voice, desire for meaningful inclusion.
4. Lies, Bullshit (BS), and Knowledge
Key Differences (Frankfurt’s Theory):
- Lie: Speaker knows the truth and deliberately conceals it.
- BS: Speaker is indifferent to the truth; only cares about persuasion or image.
- BS-er’s Goal: To manipulate, impress, or distract rather than inform.
BS in Politics:
- Common due to vague language, emotional appeal, misuse of authority, ad hominem attacks, and straw-man arguments.
- Danger: Obscures truth and misleads the public.
Guarding Against BS in Academia:
- Scientific method: Hypothesis testing, peer review, transparency in assumptions and data.
- BS fails the test of truthfulness, relevance, and logical strength.
5. Evaluating Arguments
Four Key Tests:
- Truthfulness of Premises: Are the assumptions accurate and based on evidence?
- Logical Strength: If the premise is true, is the conclusion probably true?
- Relevance: Are the reasons directly connected to the claim?
- Non-Circularity: Does the conclusion simply restate the premise?
Common Fallacies:
- Appeals to Ignorance: “We don’t know it’s false, so it must be true.”
- Appeals to the Mob: “Everyone believes it, so it must be true.”
- Ad Hominem: Attacking the person, not the argument.
- Straw Man: Misrepresenting an argument to easily refute it.
Application:
- Friedman vs. Blaydes & Linzer: Friedman is critiqued for untestable claims and anecdotal logic (BS). Blaydes & Linzer offer structured, testable empirical analysis using regression and case selection.
6. Reading Quantitative Data
Key Terms:
- Statistical vs. Substantive Significance:
- Statistical: Likelihood result is due to chance.
- Substantive: Actual importance/magnitude of effect.
- Descriptive Statistics: Summarize data (mean, median, etc.).
- Multivariate Analyses: Control for multiple variables; regression analysis reveals relationships.
Reading Tables:
- Stars *: Indicate levels of statistical significance (e.g., p < 0.05).
- N: Sample size – bigger N means more power.
- Coefficients: Direction and strength of effect (positive/negative).
- IV/DV: Know your independent (cause) and dependent (effect) variables.
- Causality: Inference must be cautious—watch for endogeneity and omitted variables.
Application:
- Understand how regression supports (or refutes) hypotheses.
- Example: Relationship between corruption and strength of democracy using cross-sectional regression.