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name: "grad-critical-realism" description: "Apply Bhaskar's critical realism to analyze phenomena through three ontological domains (real, actual, empirical), identify generative causal mechanisms via retroduction, and examine structure-agency interplay. Use this skill when the user needs to go beyond surface correlations to underlying causes, design research that distinguishes mechanisms from events from experiences, or when they ask 'what causes this beyond the observed pattern', 'what structures enable or constrain this behavior', or 'how do I move from correlation to causal explanation'." metadata: category: "WP-34 跨學科/新興" tags: ["critical-realism", "Bhaskar", "retroduction", "generative-mechanisms", "stratified-ontology", "structure-agency"]

Critical Realism (Bhaskar)

Overview

Critical realism posits a stratified ontology: the real (structures and mechanisms that exist whether or not they are activated), the actual (events that occur when mechanisms are activated), and the empirical (events that are observed or experienced). Research must move beyond empirical regularities to identify the generative mechanisms that produce observed phenomena.

When to Use

• Moving beyond correlational findings to causal explanation
• Designing mixed-methods research that integrates structure and agency
• Analyzing why the same mechanism produces different outcomes in different contexts
• Critiquing positivist or constructivist approaches as ontologically insufficient

When NOT to Use

• When pure prediction without causal understanding is sufficient
• When the research question is about subjective meaning-making only (use interpretivism)
• When the audience requires strict hypothesis-deductive methodology

Assumptions

IRON LAW: Observable events (empirical) are a SUBSET of what happens
(actual), which is a SUBSET of what exists (real) — research must go
beyond correlations to identify underlying generative mechanisms.

Key assumptions:

1. Reality exists independently of our knowledge of it (ontological realism)
2. Our knowledge of reality is always fallible and theory-laden (epistemological relativism)
3. Some explanations are better than others (judgmental rationality)
4. Causation is about mechanisms, not constant conjunctions (Humean regularity is insufficient)

Methodology

Step 1: Describe the Phenomenon (Empirical Domain)

Document observable patterns, regularities, and experiences — what do we see happening?

Step 2: Identify Events and Conditions (Actual Domain)

Map the events that occurred whether or not they were observed, including contextual conditions, co-occurring events, and counterfactual absences.

Step 3: Retroduction to Mechanisms (Real Domain)

Use retroductive reasoning: "What must be true for this phenomenon to be possible?" Propose candidate generative mechanisms — structures, powers, and tendencies that could produce the observed events.

Step 4: Assess Mechanism Activation and Context

Analyze how context enables or constrains mechanism activation. Explain why the same mechanism produces different outcomes in different settings (context + mechanism = outcome).

Output Format

## Critical Realist Analysis: [Context]

### Empirical Domain (Experienced)
- Observed patterns: [what we see in the data]
- Measurement instruments: [how we accessed these observations]
- Known limitations: [what we cannot observe directly]

### Actual Domain (Events)
- Events occurred: [including unobserved events inferred from evidence]
- Contextual conditions: [enabling/constraining factors]
- Counterfactuals: [what did NOT happen and why it matters]

### Real Domain (Mechanisms)
| Mechanism | Structure/Power | Tendency | Evidence |
|-----------|----------------|----------|----------|
| [name] | [underlying structure] | [what it tends to produce] | [how we infer it] |

### Context-Mechanism-Outcome Configuration
- Context: [specific conditions]
- Mechanism activated: [which mechanism and why]
- Outcome: [what was produced]

### Implications
1. [What this tells us about underlying reality]
2. [Practical interventions targeting mechanisms, not symptoms]

Gotchas

• Do NOT conflate mechanisms with variables — mechanisms are real structures with causal powers, not statistical predictors
• Retroduction is not induction or deduction — it asks "what must exist" not "what do we observe" or "what follows logically"
• The empirical domain is the smallest layer, yet most research stops here
• Critical realism is a philosophy of science, not a method — it is compatible with qualitative, quantitative, and mixed methods
• Structure and agency are analytically distinct but temporally intertwined (morphogenetic cycle)
• Avoid the epistemic fallacy: reducing what exists to what we can know

References

• Bhaskar, R. (1975/2008). A Realist Theory of Science. Routledge.
• Sayer, A. (2000). Realism and Social Science. Sage.
• Archer, M. S. (1995). Realist Social Theory: The Morphogenetic Approach. Cambridge University Press.