🔌 Adapter Integration

Adapters are the bridge between external media environments and the MSM Analyzer. They convert raw, platform‑specific signals into normalized MSM vectors and optional metadata. The Analyzer itself is substrate‑agnostic—it does not know or care where the data comes from. Adapters ensure that every environment can be evaluated using the same structural grammar.

Adapters do not interpret content, sentiment, ideology, or topics. Their job is to extract structural signals that map cleanly onto the five MSM axes.

🧱 Role of Adapters in the MSM Ecosystem#

Adapters serve three core functions:

Translation — convert raw platform signals into MSM‑aligned primitives
Normalization — ensure all values fall within [0.0, 1.0]
Contextualization — provide optional metadata that refines invariant and mode evaluation

The Analyzer expects a consistent input shape regardless of the platform or data source.

📐 Required Output: MediaVector#

Every adapter must produce a normalized MediaVector:

{
  S: number,  // Signal Integrity
  D: number,  // Distribution Topology
  A: number,  // Attention Dynamics
  N: number,  // Narrative Coherence
  T: number   // Temporal Cadence
}

Each axis must be in the range:

0.0 = minimum expression
1.0 = maximum expression

Adapters may compute these values using any platform‑appropriate method, as long as the mapping is consistent.

🧩 Optional Output: Metadata#

Adapters may also provide metadata that helps the Analyzer refine its interpretation:

Volatility indicators (attention spikes, churn signatures)
Narrative conflict markers (semantic divergence, contradiction density)
Cadence hints (posting frequency, cycle compression)
Signal quality markers (noise ratio, distortion, missing data)
Distribution structure hints (cluster maps, centrality, fragmentation)

Metadata is optional but improves accuracy, especially in mode and transition detection.

🛰 Mapping External Signals to MSM Axes#

Adapters must translate platform‑specific signals into the five MSM axes. Examples:

Signal Integrity (S)#

Noise ratio
Verification density
Redundancy and cross‑validation
Data completeness

Distribution Topology (D)#

Network centrality
Fragmentation index
Cross‑cluster connectivity
Broadcast vs networked flow

Attention Dynamics (A)#

Engagement volatility
Spike frequency
Saturation and burnout patterns
Temporal clustering

Narrative Coherence (N)#

Semantic similarity
Topic alignment
Conflict markers
Narrative half‑life

Temporal Cadence (T)#

Posting frequency
Cycle acceleration
Compression of update intervals
Burstiness

Adapters may use any computational method—statistical, graph‑based, semantic, or heuristic—as long as the mapping is consistent.

🧭 Normalization Requirements#

All values must be normalized to [0.0, 1.0].
Normalization ensures:

Cross‑platform comparability
Stable invariant evaluation
Consistent drift measurement
Reliable transition detection

Adapters may use min‑max scaling, logistic transforms, or domain‑specific normalization.

🔄 How the Analyzer Uses Adapter Output#

Once the adapter produces a MediaVector (and optional metadata), the Analyzer:

Validates and normalizes the vector
Computes invariant strain
Classifies basin membership
Determines behavioral mode
Measures drift
Detects transitions

The adapter’s job ends once the vector is produced.
The Analyzer handles all structural interpretation.

📦 Example Adapter Output#

{
  S: 0.62,
  D: 0.48,
  A: 0.71,
  N: 0.39,
  T: 0.83,
  metadata: {
    volatility: 0.77,
    narrativeConflict: 0.52,
    cadenceAcceleration: 0.81
  }
}

The Analyzer will use the vector directly and incorporate metadata where relevant.

🧬 Adapter Philosophy#

Adapters should be:

Minimal — only extract what is structurally necessary
Consistent — use stable mappings across time
Transparent — document how each axis is computed
Platform‑agnostic — avoid assumptions about content or ideology

The MSM Analyzer is designed to work with any media environment as long as the adapter respects these principles.