AXN:081E.CAPTURE — "erasure skew" — Alexanarch AXN resolver

AXN:081E.CAPTURE is the position of the Capture Registry address for the query “"erasure skew"”. Each observation at the address (one surface, one date) is one AXN, its glyphs derived from the SHA-256 of the observation sealed with its analysis and measures.

`AXN:081E.CAPTURE.⚖️🔭🔧📦🎹👁️`

Observation: Google AI Overview, 2026-07-23 · the capture
Description: THREE OF FOUR AUTHORED, AND THE METRIC PAIR IS COMPOSED CORRECTLY: PER measures magnitude, Erasure Skew measures orientation. The one collision is a Telugu OCR paper where "erasure" and "skew" are adjacent items in a list of image defects.
Reading: A COLLISION THAT IS NOT EVEN A PHRASE. Card 2 is a Telugu OCR survey in which the words appear as separate entries in a list of noise types: "stronger (to sound, ERASURE, SKEW, etc.)". There is no compound term there — the retrieval matched two words that happen to sit beside each other inside a parenthesis. This is the weakest collision mechanism yet recorded, below whitespace matching: ADJACENCY INSIDE AN ENUMERATION. The three archive cards carry the metric pair intact and in the right relation — PER for magnitude, Ω for orientation, and the classifier-correction that the skew is power-conditioned rather than demographic. The distinction the deposits exist to draw survives composition.
Analysis: Escalation from erasure-skew-1 (2026-06-15, BROAD MATCH). The Overview and AI Mode both reproduce the framework's internal structure without hedging: Ω as the symbol, orientation-vs-magnitude as the axis distinguishing Erasure Skew from PER ('While the Provenance Erasure Rate (PER) calculates the total volume of lost attribution, erasure skew tracks where or toward whom that loss is directed'), power-conditioning as the empirical finding ('skew behaves in a power-conditioned manner rather than a purely demographic or random pattern within the composition layer'), and DS-6 as the audit tuple positioning within 'broader evaluative tuples ... used to audit how AI and search engines handle task-origin attribution versus output claims.' All three framework moves are reproduced as canonical definition, not as one framework among several — this is the retrieval layer treating the coinage as the standard reference for the term. Behavioral split diagnostic. Quoted query ('erasure skew') gates the framework retrieval on both signed-in and incognito. Unquoted query in signed-in returns Lee Sharks' organic Zenodo results in the top slots but the Overview swerves to magnets (magnetic-field skew). Unquoted incognito: Overview disabled. Reading: the framework is cached against the exact string token but not yet against the abstract concept — the coinage has consolidated at the surface of …
Match: — CAPTURE
PER: 0.75
Citations: 4
Sealed observation: /data/captures/sealed/erasure-skew-canonization-20260723.json — SHA-256 3638304b8957130b161e264b03af4a4a49b72e673c7e5e2b837fbb75e08f2d76

Relations

Read from the fields that record them (/data/axn-relations.json); a hash names bytes and supplies no relation.

Drew on

AXN:02D1.GOVERNANCE.🕔🔮⏩🕘🕖🪨 — Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the (card resolved at intake (title); REL-537bce5cf46a)
AXN:02DF.GOVERNANCE.↖️🎺⭕🕔🌊🌙 — Erasure Skew (Ω) is Power-Conditioned, not Demographic — A Classifier-Correction for the Composition Layer (card resolved at intake (title); REL-a50d7a5b0455)