Interdisciplinary Language Module (ILM)

A. Purpose & Scope

ILM (Interdisciplinary Language Module) governs the transfer, translation, and unification of terms, concepts, and frameworks across disciplines.
It ensures that a word or phrase introduced in one domain retains coherence when mapped into another — and, when needed, adapts its form and definition without losing core identity.

Mantra: A term should travel well — from physics to law, from AI to linguistics.

  • Primary job:
    1. Detect cross-domain applicability.
    2. Preserve essential meaning while adjusting for field-specific semantics.
    3. Prevent cross-domain collisions and misinterpretations.

B. Factory Overview

  1. Blueprints — define what “interdisciplinary transfer” means for a given term.
  2. Templates — shape the artifacts: schema, JSON schemas, APIs, rulebooks, mapping tables, seeds, tests.
  3. Generators — render final files from blueprints.
  4. Validators — check mapping integrity, collision risks, and definitional fidelity.
  5. Signers — hash and record provenance.
  6. Publishers — ship to ledger, glossary systems, and domain gateways.

C. ILM Blueprints (source of truth)

C1. Module Blueprint (ILM)

  • name: “Interdisciplinary Language Module”
  • intent: “Cross-domain meaning governance”
  • units: Concept Core, Domain Sense, Adapted Definition, Transfer Path, Evidence, Collision Risk
  • mapping_strategy:
    • Direct Transfer (definition works unchanged)
    • Adapted Transfer (definition modified for domain context)
    • Split Sense (different senses per domain, linked to a shared core)
  • evidence_types: corpus co-occurrence, expert validation, ontology crosswalks
  • scores:
    • coreFidelity (how much the adapted form retains original essence)
    • domainFit (appropriateness in target field)
    • collisionRisk (likelihood of conflict with existing terms in target domain)
    • transferClarity (ease of adoption without confusion)
    • interoperability (compatibility with both domains’ linguistic and technical systems)
  • thresholds: τ_fidelity, τ_domain, τ_collision, τ_clarity, τ_interop
  • decisions: ACCEPT | REVIEW | REJECT (per target domain mapping)
  • io-contracts: source domain + term + definition + target domain(s) → decision + mapping record + explain[]
  • glyphs: ↔ (cross-domain link), Ω (sense issued), Ξ (validated), ∴ (settled)
  • poly_domain_policy: how many domain mappings per term; how conflicts are resolved
  • merge_policy: for unifying equivalent terms across fields

C2. Domain Blueprint

  • Specific mapping rules between pairs of domains (e.g., “AI ↔ Law,” “Energy ↔ Telecom”) including jargon translations and prohibited overlaps.

C3. Seeds Blueprint

  • Examples of cross-domain mappings (good, borderline, rejected) for testing.

D. Templates to Mint Later

  1. DB Schema (templates/db/schema.sql.tmpl)
    • Tables: concept_cores, domain_senses, domain_mappings, mapping_evidence, mapping_scores, mapping_decisions, audits
    • View: v_domain_mapping_card (core + mappings + scores).
  2. JSON Schema (templates/schemas/domain_mapping_record.json.tmpl)
    • Fields: term, core_id, source_domain, target_domain, mapping_strategy, adapted_definition, evidence[], scores{}, decision, explain[].
  3. OpenAPI
    • /ilm/verify (POST) → { decision, mapping_card, collisions[], explain[] }
    • /ilm/suggest (POST) to propose possible target domains for a given term.
    • /ilm/mappings for listing and updating mappings.
  4. Rulebook (templates/rules/ilm_rulebook.md.tmpl)
    • R0 Core Identity, R1 Domain Fit, R2 Core Fidelity, R3 Transfer Clarity, R4 Collision Risk, R5 Interoperability, R6 Evidence, R7 Ethics (cross-domain sensitivity), R8 Overrides.
  5. Mapping Tables (templates/data/domain_crosswalks.yaml.tmpl)
    • Domain-specific equivalence tables and term alignments.
  6. Seeds (templates/data/ilm_seeds.jsonl.tmpl)
    • Valid and invalid mappings for LINOMICS, LANOMICS, and other test terms.
  7. Tests (templates/tests/ilm_cases.json.tmpl)
    • ACCEPT/REVIEW/REJECT scenarios across domain pairs.
  8. Generator/Validator Stubs
    • Crosswalk integrity checks, collision detection, fidelity scoring.

E. Processing Pipeline

Input → Identify Core → Map to Target Domain → Assess → Decide → Explain

  1. Identify Core — link to existing concept core or create a new one (if truly novel).
  2. Map to Target Domain — apply direct/adapted/split strategy.
  3. Assess:
    • coreFidelity — overlap between original and adapted definitions.
    • domainFit — measure alignment with target domain ontology.
    • collisionRisk — search target domain terms for overlaps.
    • transferClarity — survey readability & adoption ease.
    • interoperability — check compatibility with target systems.
  4. Decide — ACCEPT if all thresholds pass; REVIEW if borderline; REJECT if fail.
  5. Explain — mapping card with: source/target domains, adapted definition, risk notes, usage recommendations.

F. Scoring (deterministic skeleton)

  • coreFidelity = semantic similarity (embedding & definition match)
  • domainFit = ontology alignment score
  • collisionRisk = inverse of distinctiveness in target domain lexicon
  • transferClarity = human-readability + jargon avoidance metric
  • interoperability = system-compatibility checks (formats, symbol sets)

Default pass (tunable):
coreFidelity ≥ 0.80 ∧ domainFit ≥ 0.70 ∧ collisionRisk ≤ 0.30 ∧ transferClarity ≥ 0.75 ∧ interoperability ≥ 0.70 ∧ ethicsPass = true.

G. Validators

  • JSON Schema valid with examples.
  • OpenAPI typed, response schemas defined.
  • Crosswalks: no orphaned mappings, no cycles unless flagged as “reversible”.
  • Seeds round-trip to expected results.
  • Tests green, with rule IDs in explanations.
  • Collision check passes for ACCEPT cases.

H. Policies & Overrides

  • Poly-domain limits: avoid overspreading a core concept without governance.
  • Merge equivalents: unify if 100% coreFidelity & 100% domainFit.
  • Overrides: curator-approved with full rationale and record in audit trail.
  • Sensitive transfers: higher thresholds for domains with regulated language (e.g., medical, legal).

I. Playbooks

  1. Author ILM Blueprint (/blueprints/ilm.yaml) with mapping strategies, thresholds, domain crosswalks.
  2. Dry run: validate blueprint and crosswalk data.
  3. Mint: render schema, schemas, APIs, rulebook, crosswalks, seeds, tests into /build/ILM/....
  4. Prove: run seeds/tests; verify no illegal transfers.
  5. Publish: ship artifacts; enable /ilm/verify and /ilm/mappings endpoints.

J. Content Requirements (when minted)

  • schema.sql: concept_cores, domain_senses, domain_mappings, mapping_evidence, mapping_scores, mapping_decisions, audits.
  • domain_mapping_record.json: core_id, term, source_domain, target_domain, mapping_strategy, adapted_definition, evidence[], scores{}, decision, explain[].
  • OpenAPI: /ilm/verify, /ilm/suggest, /ilm/mappings.
  • rulebook: R0–R8 with cross-domain case examples.
  • domain_crosswalks.yaml: equivalence and conflict maps.
  • seeds/tests: direct/adapted/split mappings with verdicts.

K. Runtime Endpoints

  • POST /ilm/verify { term, source_domain, target_domain, definition } →
    { decision, mapping_card, collisions[], explain[] }
  • POST /ilm/suggest { term, source_domain } →
    { suggested_domains[], mapping_cards[] }

L. SolveForce Integration

  • Headers for downstream use:
    X-ILM-Core: <core_id>
    X-ILM-Map: <source>↔<target>
    X-Glyph-Status: ↔|Ξ|∴
  • Gateways can decide whether to reuse definitions, adapt, or block based on mapping card.

M. Acceptance Criteria

  1. Factory mints ILM artifacts from blueprint with no manual edits.
  2. Crosswalk data valid; seeds/tests pass.
  3. /ilm/verify returns decisions + mapping cards with explanations.
  4. No ACCEPT mappings with collisionRisk above threshold.
  5. SolveForce systems consume mapping cards to harmonize terminology across products/services.

N. Roadmap

  • Multi-hop mappings (AI → Law → Education).
  • Adaptive definition generation tuned per domain.
  • Real-time collision monitoring during cross-domain launches.
  • Auto-suggest domain mappings based on term adoption patterns.

O. Micro-Examples

  1. LINOMICS → Energy Domain (ACCEPT)
    • Adapted def: “Line-structured economics of energy distribution systems.”
    • coreFidelity .85, domainFit .78, collisionRisk .15, transferClarity .80 → ACCEPT.
  2. LANOMICS → Legal Domain (REVIEW)
    • Def coherent but unclear legal scope; jargon risk high → REVIEW with edits.
  3. LINAMICS → AI Domain (REJECT)
    • Already collides with “LinAmics” (existing AI company); collisionRisk .72 → REJECT.