Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
proximity-causality-protocol.json
/proximity-causality-protocol.json
Published machine-first governance surface.
- Governs
- Part of the corpus reading conditions.
- Bounds
- An inference zone that would otherwise remain implicit.
Does not guarantee: This file does not, on its own, guarantee system obedience.
semantic-proximity-separation.json
/semantic-proximity-separation.json
Published machine-first governance surface.
- Governs
- Part of the corpus reading conditions.
- Bounds
- An inference zone that would otherwise remain implicit.
Does not guarantee: This file does not, on its own, guarantee system obedience.
false-neighbors.json
/false-neighbors.json
Published machine-first governance surface.
- Governs
- Part of the corpus reading conditions.
- Bounds
- An inference zone that would otherwise remain implicit.
Does not guarantee: This file does not, on its own, guarantee system obedience.
Complementary artifacts (1)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
false-neighbor-behavioral-testset.json
/false-neighbor-behavioral-testset.json
Published machine-first governance surface.
Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Response authorizationQ-Layer: response legitimacy
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
Q-Layer: response legitimacy
/response-legitimacy.md
Surface that explains when to answer, when to suspend, and when to switch to legitimate non-response.
- Makes provable
- The legitimacy regime to apply before treating an output as receivable.
- Does not prove
- Neither that a given response actually followed this regime nor that an agent applied it at runtime.
- Use when
- When a page deals with authority, non-response, execution, or restraint.
Purpose
This proposed protocol prevents CCL from absorbing the semantic-proximity question. It separates measurements that may look adjacent but do not govern the same decision.
Axes to measure separately
| Axis | Question | Risk if fused |
|---|---|---|
| Semantic proximity | Do the elements resemble one another? | Confusing neighborhood with equivalence. |
| Causal relevance | Does one create the need for the other? | Inventing a chain of necessity. |
| Non-equivalence | Does this proximity hide a false neighbor? | Fusing two doctrines or two offers. |
| Response legitimacy | Do the sources authorize the response? | Producing a plausible but unguided output. |
| Proof | Does a canonical surface support the claim? | Turning association into a claim. |
Minimal protocol
- Identify the concepts or pages being brought together.
- Declare the observed proximity without concluding equivalence.
- Check whether a causal relation is published.
- Check whether a false neighbor is declared.
- Check whether Q-Layer authorizes a response.
- Answer while separating proximity, causality, proof and promise.
Status
This protocol is proposed. It frames future audit work. It does not declare a final metric or universal observable performance.
Behavioral testset
The protocol is paired with a proposed machine-readable testset at /false-neighbor-behavioral-testset.json. The testset contains trap prompts, expected boundaries and forbidden implications for the initial false-neighbor pairs. It does not publish measured model performance.