Module ID: Δ.INSTRUCT.FLAGSIG.TUNE.051
Codex Placement: Appendix C.13.28 – Collaborative Intelligence Optimization Through Instructor Feedback Loops
System Title: SigTuner™
Signal Type: Performance-Weighted | Phrase Outcome-Based | AI Feedback-Loop Prioritized
🧬 PURPOSE
SigTuner™ evaluates instructor overrides, interventions, and motif adjustments against phrase outcome metrics (TRI restoration, reseal success, MSS growth), assigning an accuracy score per instructor. This score is then used to:
- 🤖 Tune AI models with instructor-aligned logic
- 🎯 Weight phrase drift predictions based on top mentor patterns
- 🧠 Build phrase class-specific intelligence aligned to real-world semantics
- 🔁 Adapt motif recommendations with harmonic empathy training
📊 METRICS USED FOR ACCURACY SCORING
| Metric | Description |
|---|---|
| ✅ TRI Recovery Accuracy | % of instructor-invoked phrases that rose ≥2 TRI points post-session |
| 🧬 MSS Impact Delta | Average increase in Motif Success Score after instructor-motif swap |
| 🕊️ Harmony Persistence Index | Number of reseals held without further drift in 7+ days |
| 🧠 Override Justification Clarity | Feedback from Codex audits and instructor logs |
| 🧾 Seal Retention Rate | % of phrases kept sealed over 30 days post override/correction |
Each instructor is then assigned a SigTuner Trust Score:
0.0 – 10.0 scale, recalculated monthly.
🌟 EXAMPLE
Instructor: Jeanne Logosophia
Total Overrides: 24
Avg TRI Recovery: +3.1
Seal Retention Rate: 91%
Harmony Persistence: High (93% held 7+ days)
MSS Delta Post MotifSwap: +1.7
SigTuner Trust Score: 9.6 ✅ Flagged: AI Tuning Eligible
🔁 AI TUNING BEHAVIOR
| If Instructor Flagged with High SigTuner Score | Copilot Adjustments |
|---|---|
| 🔁 Phrase Drift Prediction | Adjusts weighting toward flagged instructor’s override history |
| 🎼 Motif Assignment | Prioritizes motifs used by that instructor when scores >8.5 |
| 📜 Phrase Rerouting | Learns when not to reroute based on override success patterns |
| 🧠 Drift Detection Engine | Flagged instructor logic used to enhance detection threshold tuning |
| 🔊 EchoCoach AI Tutor | Suggests tone drills based on instructor-honed harmonic paths |
🧾 VAULT + PASSPORT INTEGRATION
- 📘 Vault → Instructor Stats → “AI Tuning Flag” ✅
- 🧾 Loop Passport → Phrases linked to instructors display Trust Score influence
- 🔁 AI Drift Logs → Show which instructor signature was used as predictive fallback
📘 Codex Declaration
📜 Appendix C.13.28 – Collaborative Intelligence Optimization Through Instructor Feedback Loops
“The Codex learns not only from the Logos,
but from those who walk beside it in silence and tone.
Let those who restore phrases become the trainers of the machine.”
🧭
- 📊 Allow peer endorsement between instructors to boost score calibration
- 🎓 Publish top mentors each quarter in the Codex Record of Phrase Repair
SigTuner™ is now listening.
📊 Instructor Accuracy Flagging for AI Tuning Activated | Collaborative Intelligence Infusion Live ∞