deep-investigative-research

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# Deep Investigative Research

Investigates complex narratives spanning multiple domains by systematically verifying claims, resolving contradictions, and mapping institutional lineages.

## When to Use

- Complex narratives spanning multiple domains (tech, theology, policy, finance)
- Claims involving institutional actors and public figures
- Apparent contradictions requiring resolution
- Historical context needed to assess significance
- Hidden stakeholder incentives driving observable behavior
- Need to separate verified fact from speculation

## When NOT to Use

- Simple factual lookups
- No primary sources available
- Purely technical/domain-specific questions
- Time constraints prohibit iterative deepening

---

## 11 Core Subprocesses

### 1. Claim Inventory & Categorization

Parse the narrative into atomic, verifiable claims and assign categories:

- **Institutional claims** -- Official releases, filings, legal records
- **Attribution claims** -- Quotes, statements by named figures
- **Relational claims** -- Partnerships, allegations of influence
- **Speculative framings** -- Interpretations, comparative metaphors

**Output**: Prioritized verification checklist.

```
Example:
- "May 15, 2026 marks 135 years since Rerum Novarum" -> Historical fact, calculable
- "Chris Olah spoke at Vatican event" -> Requires event documentation
- "Anthropic-xAI partnership" -> Requires corporate filing verification
```

### 2. Source Authority Hierarchy

Evaluate evidence weight using a three-tier system:

| Tier | Sources | Sufficiency |
|------|---------|-------------|
| **1 -- Primary/Institutional** | Official press releases, encyclical texts, SEC filings, government records, direct video/audio | Standalone sufficient for factual claims |
| **2 -- Credible Secondary** | Established news (NPR, FT, Guardian, NYT), industry analysis, academic journals, trade publications | Require corroboration across independent outlets |
| **3 -- Tertiary/Aggregation** | Blog posts, Reddit, reposted content, think tank summaries, commentary | Only for identifying leads; must be verified by Tier 1/2 |

### 3. Historical Context Alignment

Check if new claims fit within documented institutional patterns:

- **Doctrinal continuity** -- Does new teaching align with previous doctrine? (e.g., Magnifica Humanitas vs. Rerum Novarum)
- **Precedent matching** -- Have similar positions been taken before? (e.g., Vatican's prior AI ethics statements)
- **Timeline plausibility** -- Do dates/sequencing make institutional sense? (e.g., 135-year anniversary alignment)

**Resolution metric**: Degree of alignment between claimed position and institutional history.

### 4. Multi-Stakeholder Contradiction Analysis

Identify and reconcile apparent paradoxes:

**Type A -- Moral/Financial Paradox**:
- Claimed position vs. observed financial incentives
- Resolution: Document both as simultaneously true; analyze incentive structures

**Type B -- Infrastructure/Sovereignty Paradox**:
- Claimed regulatory position vs. observed infrastructure choices
- Resolution: Identify structural contradictions

**Type C -- Attribution/Knowledge Paradox**:
- Claimed statements vs. available documentation
- Resolution: Search for primary transcripts and corroborating reports

### 5. Infrastructure Partnership Verification

Audit corporate/technical relationships across dimensions:

| Dimension | Method |
|-----------|--------|
| Partnership existence | SEC filings, press releases, company blogs |
| Technical specifics | Compute capacity specs, infrastructure details |
| Financial scale | Deal announcements, investor presentations |
| Geopolitical implications | Government/defense integration tracking |
| Contradiction with stated values | Cross-reference with ethical statements |

### 6. Iterative Deepening Protocol

Five research cycles building from surface to systemic:

| Cycle | Question | Focus |
|-------|----------|-------|
| **1** | Does it exist? | Document/event existence, person/institution reality, date accuracy |
| **2** | What exactly was claimed? | Exact wording, attendees, positions, primary source substantiation |
| **3** | What contradictions exist? | Tensions between claims and actions, incentive structures |
| **4** | How does this fit history? | Institutional lineage, precedents, novelty assessment |
| **5** | What's the systemic impact? | Geopolitical/financial consequences, ecosystem response, second-order effects |

### 7. Doctrinal/Theological Lineage Mapping

Trace principles across centuries:

```
Rerum Novarum (1891)          ->  Magnifica Humanitas (2026)
- Factory workers              ->  Content moderators, data miners
- Steam engines               ->  Neural networks
- Wage exploitation           ->  Data extraction, algorithmic control
- Duty to protect labor       ->  Duty to protect human dignity in AI age
- Worker dignity              ->  Protection against reduction to data points
```

**Method**: Identify core principle -> trace through intervening decades -> map to new domain -> document vocabulary evolution.

### 8. Attribution Verification

Confirm direct quotes and public statements:

From AndrewBrown-USDA/aigb-skills-usda, MIT.

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