subjective-label

name	subjective-label
description	Human-grounded workflow for labeling a large text corpus on a vague subjective trait while jointly refining H/L/N gold labels, seven diagnostic regions, a human-and-machine-readable guideline, sealed-test model scorecards, and production provenance. Use for subjective annotation projects, calibration rounds, guideline optimization, final evaluation, corpus completion, or /subjective-label.

Subjective Label router

Route the request to the smallest canonical command that can advance the project.

Canonical commands

command	responsibility
/sl-init	initialize corpus, seal final-test ids, embed the development pool, run random Round 1, and close the first checkpoint
/sl-round	run or resume one later calibration round from candidate retrieval through checkpoint
/sl-evaluate	freeze G*, create blind human gold on the sealed test, and score registered executors
/sl-complete	run a validated production policy over the remaining corpus, reconcile outcomes, and perform final audit
/sl-status	inspect current state, evidence, holds, and next valid action without writing

Compatibility aliases:

• /sl-iterate routes to /sl-round;
• /sl-validate routes to /sl-evaluate;
• /sl-scale routes to /sl-complete.

Explain the changed semantics when an alias is used. Do not reproduce the legacy panel-majority, public-dataset convergence, or k-NN inheritance workflow.

Knowledge commands remain available when installed: lesson, feedback, and digest.

Core contract

Treat one identified human as the semantic authority. Use the strong calibration agent to elicit, contrast, record, and generalize that person's judgment. Use weak language models as independent executors whose sealed outputs diagnose guideline clarity; never treat their consensus as gold.

Maintain these outputs:

• cumulative human-confirmed calibration gold D_t, frozen as D_cal* at stopping;
• completed corpus D* only after validated production, reconciliation, and audit;
• versioned structured guideline G_t and eventually G*;
• seven diagnostic regions H, L, N, HL, LN, HN, and HLN, separate from uncertainty;
• complete Session, checkpoint, test, scorecard, production, and provenance records.

The canonical round is:

closed G_(t-1)
  → candidate pool C_t
  → sealed weak prelabels P_t
  → human batch B_t
  → blind Human-AI Session
  → human final Y*_t + guideline draft
  → checkpoint
  → closed D_t + G_t

Round 1 is random and has no model prelabels or inherited regions. Later rounds combine targeted challenge items with a stratified random sample of consensus items to detect shared model error.

Required references

Before routing a mutating command, read the command skill and the references it names. The authority and lifecycle sources are:

• ../../ref/ref-contract.md
• ../../ref/ref-stages.md
• ../../ref/ref-assets.md
• ../../ref/ref-architecture.md

Safety and implementation truth

Inspect the actual project state and available implementation before writing. Never claim a phase ran because its conceptual contract exists. When a required engine, custodian, schema writer, or verifier is absent, write no substitute evidence; return a structured HOLD with the missing capability, preserved state, and next implementation action.