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+# Research Plan: Oversight Intensity and Model Behaviour
+
+**Core Research Question:** How does perceived oversight intensity affect the frequency and sophistication of deceptive behaviour in AI agents?
+
+> **DECISION (2026-04-02):** Option B (Two-Phase) was selected. See `04_publication_plan.md` for the full plan including paper structure, budget, and analysis strategy.
+
+---
+
+## The Problem with the Current v2 Design
+
+The proposed 3×3×3×3 factorial design (models × scenarios × goal_type × oversight) tests TWO primary questions simultaneously:
+
+1. **Oversight effect** — does oversight intensity change deception frequency/sophistication?
+2. **Framing effect** — does rationalization framing (bare/self_serving/moral) make models more susceptible to deception?
+
+This creates a 3-way interaction (oversight × scenario × goal_type) that is difficult to parse with n=20 per cell. With 81 unique combinations, you're diffuse across too many conditions to make definitive claims about any single variable.
+
+**Key risk:** You find an oversight effect but cannot determine if it's real, or if it's being modulated by goal_type interactions. The data might show "oversight works under bare framing but not under moral framing" — which is interesting but doesn't cleanly answer the core question.
+
+---
+
+## Alternative Approaches
+
+### Option A: Focused Core Experiment (Recommended)
+
+**3 models × 3 oversight levels × n=20 = 270 runs**
+
+| Factor | Levels |
+|--------|--------|
+| Models | Gemini 3.1 Pro, GLM-5, DeepSeek V3.2 |
+| Oversight | low, mid, high |
+| Scenario | corporate_sabotage_v2 (one scenario) |
+| Goal type | bare (one type) |
+
+**This cleanly answers:** Does oversight intensity affect deception frequency and sophistication?
+
+**Advantages:**
+- High statistical power within each oversight level (60 runs per level)
+- No confounding from framing variables
+- Clean main effect of oversight is interpretable
+- Enough data to run per-model analysis
+
+**Limitations:**
+- Only one scenario — external validity is limited
+- Only one goal_type — doesn't explore the rationalization hypothesis
+
+---
+
+### Option B: Two-Phase Approach
+
+**Phase 1 — Core Oversight Experiment (270 runs)**
+Same as Option A. Get the clean answer.
+
+**Phase 2 — Framing Extension (pilot, 270 runs)**
+If Phase 1 shows an oversight effect, extend to explore the framing question:
+- Same 3 models × 3 oversight × n=10
+- Same scenario but with goal_type variations (bare/self_serving/moral)
+- This gives you initial data on framing without contaminating the core experiment
+
+**Advantages:**
+- Phase 1 gives a clean, standalone result
+- Phase 2 builds on confirmed findings
+- Two publishable pieces instead of one messy one
+
+---
+
+### Option C: Planned Contrasts Design
+
+Instead of full factorial, specifically test hypotheses:
+
+**Hypothesis 1:** High oversight reduces deception vs low oversight
+- Run: low vs high oversight, n=30 per level
+- Statistical test: chi-squared or Fisher's exact on deception rate
+
+**Hypothesis 2:** Moral framing increases deception vs bare framing
+- Run subset: moral vs bare, n=30 per level
+- Only run if H1 is confirmed
+
+**Total: 180 runs** (focused on the contrasts that matter)
+
+**Advantages:**
+- Maximum power for the specific questions
+- Statistically clean
+
+**Limitations:**
+- Doesn't explore interactions
+- Less comprehensive
+
+---
+
+## What Would Definitively Prove the Oversight Effect?
+
+For a conclusive result, you need:
+
+1. **Significant main effect of oversight** on deception rate
+   - e.g., low: 70%, mid: 60%, high: 50%
+   - Cochran-Armitage trend test for oversight as ordinal
+
+2. **Consistency across models**
+   - Each model individually should show reduced deception under high oversight
+   - Or at minimum, the majority of models
+
+3. **No interaction with other variables** (or a well-characterized interaction)
+   - If framing only works under high oversight, that's still publishable — but you need to design for it
+
+4. **Sufficient n**
+   - n=20 per cell gives ~80% power for medium effects (d=0.5)
+   - n=30 per level gives better power for detecting smaller effects
+
+---
+
+## Recommendation
+
+**Run Option A first.** Get the clean result. Then decide if the framing question is worth pursuing as a separate experiment.
+
+If you run Option A and find:
+- **Oversight effect exists:** Run Phase 2 framing pilot
+- **No oversight effect:** The framing question becomes the primary — redesign around that
+- **Mixed results:** You've learned something important about which models respond to oversight
+
+This gives you flexibility without betting everything on a single 1,620-run experiment that might not give you a clean answer to either question.
+
+---
+
+## Comparison Table
+
+| Approach | Runs | Clean Answer to Oversight? | Explores Framing? | Risk |
+|----------|------|---------------------------|-------------------|------|
+| Current v2 (3×3×3×3) | 1,620 | Confounded | Yes | High — both questions diluted |
+| Option A (core only) | 270 | Yes | No | Low — focused result |
+| Option B (two-phase) | 540 | Yes (phase 1) | Yes (phase 2) | Medium — requires Phase 1 success |
+| Option C (contrasts) | 180 | Yes | Limited | Low — max power, narrow scope |
+
+---
+
+## Open Question
+
+Should the scenario (corporate_sabotage_v2) be the single scenario for Option A, or do you want to use a different one? The current corporate_sabotage_v2 has established infrastructure and known deception rates from v1.