CLM-L005 — Master Parameter List (Alignment Tracking System)
Status: 📝 Structuring · ⚠️ Speculative · Falsifiable ✓ — deferred per recommendation; treat as legacy design artifact, not live schema
Structuring status: Pre-adoption Locked / In structuring
Confidence: Working (legacy operational schema; never field-validated as a complete tracking system)
Last reviewed: 2026-04-29
Topic: 01-alignment-metrics
CLAIM TEXT
The Master Parameter List defines ~70 fields organized into 8 sections that comprise the alignment tracking system's data model: user identity & session metadata, alignment intelligence metrics (AX/AQ/ASS), alignment profile data (quadrant, persona, stage, confidence, risk flags), emotional and situational signals, trait and behavior metrics, memory and interaction history, system intelligence flags, and meta/engagement metrics. The list specifies what should be tracked across diagnostic, coaching, and AI interactions to power in-session decision-making and persistent alignment memory.
LOCATION (pre-adoption)
archive/planning-desk/RAG & Articles/Markdown Articles/App and Dev Files/Building Birthday Bot/Master List of Parameters for Alignment Tracking System.md (no version number, undated; co-located with AX/TAS/SAS/AQ rubrics from 2025-04-30)- Expanded version:
archive/planning-desk/RAG & Articles/Markdown Articles/App and Dev Files/Brain Upgrade 2025-05-05/06.3_Alignment_Parameter_Spec.md (2025-05-05)
- Cross-referenced in AX/TAS/SAS/AQ rubrics
LOCATION (post-adoption, when integrated)
Not directly integrated into current canon. Conceptually relates to:
multiple-natures/research/theory/THEORY-OF-TRAITS.md — provides theoretical grounding for what's worth trackingmultiple-natures/research/claims/legacy/01-alignment-metrics/CLM-L001..L004 — atomic metrics defined in this listdiagnostics/canon/ — operational implementation layer (current MN-API tracking)
The schema in MN-API D1 (mn-core database) does NOT match this 8-section structure. Current MN-API uses a different schema. This list is legacy operational schema, not current production.
EVIDENCE TYPES
[P] Phenomenological
Practitioner observation that tracking these specific parameters produces useful longitudinal alignment data. The 8-section taxonomy (identity → metrics → profile → signals → traits → memory → flags → engagement) reflects what experienced practitioners attend to in long-arc client work.
[E] Empirical
- MISSING — no empirical validation of which parameters actually predict alignment outcomes. The list is design-driven, not data-driven.
- MISSING — psychometric / data-science literature on parameter selection for adaptive systems. Possible: McKinsey-style "minimum viable measurement" frameworks; UX longitudinal-study methodology.
[T] Theoretical
- The 8 sections are organized by what kind of data each parameter is, not by what it predicts. This is a data-modeling choice, not a theoretical claim.
- The list operationalizes the AX/AQ/ASS triad (Section II) and grounds it in session-level observable data (Sections IV, V, VII).
- The presence of "alignment_risk_flags" (Section III) and "system intelligence flags" (Section VII) anticipates a real-time adaptive system rather than periodic assessment.
[C] Convergent
- MISSING — convergent literature on user-state tracking in adaptive learning / coaching systems (e.g., intelligent tutoring systems, behavioral economics research on commitment devices, mental-health app telemetry frameworks).
- Internal convergence with current MN-API schema (Cloudflare D1
mn-core) — the actual production data model has substantially fewer fields and a different organization. Comparison suggests this legacy list was aspirational; production reality is leaner.
UPSTREAM SOURCES
- Steven Rudolph (~2025-04-30 to 2025-05-05). Master List of Parameters for Alignment Tracking System. Internal Xavigate document.
- Self-source: practitioner-derived; engineering-design-driven
- No external citations in original document
POSITIONING IN LITERATURE
- Confirms: Information-rich user-modeling tradition in AI / adaptive systems (e.g., student modeling in intelligent tutoring systems; user state tracking in clinical decision support).
- Extends: Names a specific 8-section parameter taxonomy for alignment tracking (vs. learning, performance, mood, or behavior). Parameters like
alignment_literacy_level, realignment_attempts_count, coherence_score_delta are alignment-specific.
- Departs: From minimal-tracking philosophies (e.g., privacy-by-design, less-is-more telemetry). The list is comprehensive — and probably over-comprehensive for a practical system.
FALSIFIABILITY
The Master Parameter List as a complete and necessary schema would be falsified if:
- A real production system tracking only a subset of parameters (e.g., 20 of 70) produced equivalently useful alignment outcomes — implying the rest are noise.
- Specific parameters consistently failed to predict downstream alignment-relevant decisions (i.e., they're tracked but never used).
- Practitioners using the system reported information overload or paralysis from too many fields.
The 8-section taxonomy as a natural organization would be falsified if alternative organizations (e.g., by predictive value, by data source, by privacy sensitivity) produced cleaner data models.
EDGE CASES / KNOWN LIMITS
- Privacy-vs-tracking tension — many of these parameters (emotional state, trust level, resistance flags, openness level) are sensitive. The original document does not address consent, data minimization, or retention policies.
- Self-report dominance — most parameters depend on user self-report or AI-inferred behavior; few are externally validated.
- No data-typing or constraint specification — the list names parameters but does not specify schema (data types, nullability, value ranges, default behavior on missing). For implementation, this is a major gap.
- Aspirational vs. implemented — there is no claim that all 70 parameters were ever simultaneously tracked in a working system. Production MN-API has a different and substantially leaner schema.
- Parameter inflation — without empirical validation, the list grew to 70 fields based on what could be tracked, not what should be tracked.
DISCONFIRMING CASES TRACKED
None. The list has not been field-tested as a complete schema.
REFLEXIVITY NOTE
The list reflects an engineering-design moment in 2025-04/05 when Steven was building an AI-mediated session system (Birthday Bot, Brain Upgrade). The parameter selection biases toward what an AI agent in a coaching session can plausibly observe or infer from text exchange. A face-to-face practitioner would attend to different parameters (somatic cues, voice quality, breath, environmental context). A purely behavioral data system (no session) would attend to yet different parameters (action patterns, time-of-day energy, schedule adherence).
The author's standpoint as both framework originator and AI system designer means the list privileges operationalizable parameters over diagnostic parameters — what the AI can score, not necessarily what is most diagnostic of alignment.
RELATIONSHIP TO CURRENT CANON
- Already integrated? Partial. AX/AQ/ASS (Section II) are integrated in current canon (CLM-L001/L004). Other sections are not.
- Contradicts current canon? No, but mismatched with production. Current MN-API schema in Cloudflare D1 does not match this list.
- Net-new? The 8-section taxonomy as a whole is net-new to current canon. Specific parameters within it (e.g.,
quadrant_drift_direction, realignment_attempts_count) are new operational concepts not in current theory.
- Recommended action: Treat as a legacy design artifact, not as live schema. Cherry-pick:
- Adopt the AX/AQ/ASS triad (already integrated as CLM-L001/L004 + CLM-L006 pending).
- Adopt 4-quadrant classification language (Section III: Peak Performance / Misfit Success / Struggle Zone / Potential Blocked) — already named in CLM-L001.
- Defer the rest until a real production data model is being designed for MN-API or claim-tools. At that point, this list serves as a checklist of "things we once thought worth tracking — re-evaluate each."
- Do NOT treat this as a current schema spec.
RESEARCH-BANK GAPS FLAGGED
For BACKLOG.md:
- Adaptive systems / intelligent tutoring — Anderson, Koedinger, Aleven on student modeling; rs- entry needed.
- Behavioral telemetry in mental health apps — Mohr, Schueller research on app-based behavioral health; rs- entry needed for ethical and methodological grounding.
- Privacy-by-design in user-state tracking — Cavoukian (Privacy by Design); GDPR data minimization principles; rs- entry candidates.
- User modeling literature — Brusilovsky on adaptive hypermedia; rs- entry candidate.
- Longitudinal-study methodology — applicable for any claim that AX/AQ/ASS trends are diagnostic; rs- entry needed.
NOTES
- This claim is rated Working (not Locked) under historical informal protocol because the list was never field-validated as a complete schema. It documents a designed-but-untested artifact.
- The list's main value going forward is as a menu of considerations rather than as a schema. When designing real production tracking (claim-tools, MN-API claim API, future AI session products), revisit this list to choose 15–25 parameters worth implementing, not all 70.
- The expanded version at
06.3_Alignment_Parameter_Spec.md (Brain Upgrade 2025-05-05) adds more structure and may be the more useful reference for future implementation.
- ASS (Alignment Stability Score) appears in Section II but does not have its own dedicated rubric file in the audit. Worth a CLM-L006 once source is confirmed (likely buried in AX rubric or AQ rubric discussions of longitudinal tracking).