MCCF Application: Coherence-Based Detection of Signal Drift

1. Problem Definition

We are given multiple representations of the same underlying source:

• Original source (S₀)
• Derived signals (S₁, S₂, … Sₙ):
  • summaries
  • headlines
  • commentary
  • model outputs

Each transformation may introduce:

• emphasis shifts
• framing bias
• omission or amplification

We want to detect:

When does transformation become manipulation?

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2. Core MCCF Idea

Treat each version as a projection of an underlying latent meaning field.

MCCF asks:

Do these projections remain coherent across channels, or do they diverge?

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3. Representing the Signal

Each signal Sᵢ is mapped into a feature vector:

A. Semantic Core

• key propositions (claims)
• entities
• relationships

B. Affective Field

• tone (neutral / alarmist / reassuring)
• emotional valence
• urgency

C. Intent Vector (inferred)

• inform
• persuade
• alarm
• normalize

D. Salience Map

• what is emphasized
• what is omitted

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4. Coherence Metric

Define pairwise coherence between signals:

$$C(S_i,S_j)=w_1\cdot \text{semantic\_overlap}+w_2\cdot \text{affective\_alignment}+w_3\cdot \text{intent\_alignment}+w_4\cdot \text{salience\_similarity}$$

Where:

• values range from 0 (divergent) to 1 (aligned)

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5. Field Coherence

Across all signals:

$$C_{field}=\frac{2}{n(n-1)}\sum _{i<j}C(S_i,S_j)$$

This gives:

Global coherence of the information field

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6. Drift Detection

Now the key move:

Define baseline coherence relative to the source S₀:

$$D_i=1-C(S_i,S_0)$$

• Low Dᵢ → faithful transformation
• High Dᵢ → drift

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7. Manipulation Signature

Not all drift is manipulation.

Manipulation tends to show a distinct pattern:

A. Directional Drift

Multiple signals drift in the same direction

B. Selective Amplification

• specific aspects exaggerated
• others consistently suppressed

C. Affective Skew

• tone shifts without semantic justification

D. Intent Realignment

• shifts from “inform” → “persuade” or “influence”

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8. MCCF Insight: Coherence vs Alignment

Here’s the subtle but critical distinction:

• High alignment ≠ high coherence
• A coordinated campaign can be:
  • internally consistent
  • but divergent from source truth

So we track:

• Internal coherence: C(Sᵢ, Sⱼ)
• Source coherence: C(Sᵢ, S₀)

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9. Practical Example (Your Case)

Signals:

• S₀: arXiv paper
• S₁: article headline (“tested on real people with money”)
• S₂: my initial framing
• S₃: your interpretation

You observed:

• S₁ → high affective amplification
• S₂ → institutional normalization
• Both:
  • preserve facts
  • shift salience

So:

• C(S₁, S₀): moderate (emotionally skewed)
• C(S₂, S₀): moderate (legitimacy skewed)
• C(S₁, S₂): low (different directional drift)

That’s a low-coherence field with competing projections.

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10. Operational Use

This becomes a system:

Input:

• multiple texts about same topic

Output:

• coherence score
• drift vectors
• manipulation likelihood

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11. Visualization (Conceptual)

Think of it as a field:

• center = latent truth
• signals = vectors in space

Coherent system:
→ tight clustering

Manipulated system:
→ directional stretching / polarization

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12. Why This Matters

This gives you:

A way to measure epistemic integrity across an information ecosystem

Instead of asking:

• “Is this true?”

You ask:

• “Is this coherently derived from the same source?”

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13. Extension to Your Work

This fits directly into MCCF:

• channels = sources / models / humans
• field = shared meaning space
• coherence = stability of interpretation

And crucially:

You can run this in real time inside a human-AI loop

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14. The Deeper Insight (your intuition formalized)

What you said informally:

“Signals distant from the source subtly shift meaning”

Formalized:

Information degradation is not random—it follows structured, directional drift in feature space