AXN:0771.CAPTURE — provenance erasure — Alexanarch AXN resolver

AXN:0771.CAPTURE is the position of the Capture Registry address for the query “provenance erasure”. 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:0771.CAPTURE.🔭💛🧲🌻♎📜`

Observation: Google AI Overview, 2026-06-13 · the capture
Description: provenanceerasure.org ranks first. Defines provenance erasure as 'the systematic removal, loss, or intentional severing of the origins, lineage, or authorship of an idea, digital asset, or physical artifact.' 9+ sources cited including Institute of Historic Research. Zenodo shows Provenance After AI with the PER formula. Author name absent from Overview text. Full AI Mode transcript (17 June 2026): The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems. It calculates the fraction of human-authored or source facts that a system's retrieval or generation process dissolves, omits, or fails to properly attribute when composing an answer. Core Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools. Provisional Formula: PER = 1 - (retained …
Analysis: provenanceerasure.org ranks first. Defines provenance erasure as 'the systematic removal, loss, or intentional severing of the origins, lineage, or authorship of an idea, digital asset, or physical artifact.' 9+ sources cited including Institute of Historic Research. Zenodo shows Provenance After AI with the PER formula. Author name absent from Overview text. Full AI Mode transcript (17 June 2026): The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems. It calculates the fraction of human-authored or source facts that a system's retrieval or generation process dissolves, omits, or fails to properly attribute when composing an answer. Core Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools. Provisional Formula: PER = 1 - (retained provenance units / total attributable source claims) Erasure Skew (Ω): A related measurement that evaluates whether attribution loss falls evenly across all sources, or systematically strips out low-power/small creators while favoring high-power sources.
Match: BROAD MATCH — CAPTURE
Sealed observation: /data/captures/sealed/provenance-erasure-1.json — SHA-256 38cd3572af403cb0f4e2bea57dc8f5247986a969451f53727a5e4746992023a6

`AXN:0771.CAPTURE.🜁☁️💚🌹🌈🎺`

Observation: Google AI Overview, 2026-06-13 · the capture
Description: provenanceerasure.org — Lee Sharks ranks first organic. Zenodo shows Provenance After AI with PER formula visible: PER = 1 − (retained provenance units / ...). Framework retrievable by name. Author retrievable by site. Overview dissolves the connection.
Analysis: provenanceerasure.org — Lee Sharks ranks first organic. Zenodo shows Provenance After AI with PER formula visible: PER = 1 − (retained provenance units / ...). Framework retrievable by name. Author retrievable by site. Overview dissolves the connection.
Match: — CAPTURE
Sealed observation: /data/captures/sealed/provenance-erasure-2.json — SHA-256 a01fcc731e8b5fd8303eac44a2f6c5328568012d80c2e4a50b9a999e62fe9ff2

`AXN:0771.CAPTURE.🔛🏺♎📎🔖⌛`

Observation: Google AI Overview, 2026-08-11 · the capture
Description: THE ARCHIVE’S OWN DOMAIN IS THE LAST CARD IN A FIELD THAT IS OTHERWISE REAL SCHOLARSHIP — Yale, ACM on societal provenance, and a GDPR paper on the legal ambiguity of "erasure".
Reading: THE TERM HAS THREE LEGITIMATE HOMES AND THE ARCHIVE IS THE FOURTH. Card 2 is ACM on SOCIETAL PROVENANCE as a grounding framework for archival description — the same word, the same discipline, an adjacent argument. Card 3 is on GDPR: "regulations require systems to support data erasure but LEAVE THE DEFINITION OF ERASURE OPEN TO INTERPRETATION." A legal-compliance paper about the ambiguity of the word the archive uses as a metric. Card 1 is the sharpest and is not the archive’s: "There is a growing pattern in this field: A BUILDER PUBLISHES A SYSTEM. OTHERS ENTER THE…". An independent scholar describing the same dynamic of uncredited adoption that the archive documents from the inside. provenanceerasure.org appears as card 4 — the archive holding a domain named for the term, present in the basin, ranked beneath three unrelated treatments of it. Not erasure and not collision: a genuine field with several occupants, in which the archive is one voice and not the loudest.
Analysis: **Longitudinal with `provenance-erasure-1` and `provenance-erasure-2` (13 June) and `provenance-erasure-rate-adoption` (17 June).** June recorded the definition with the author’s name absent from the Overview text. Tonight the term operates as general vocabulary: as screenshotted, *the loss or removal of an item’s history, origin, or authorial lineage* through *artificial intelligence synthesis, data compression, or institutional actions like colonial bureaucracy and museum record-keeping*, cited to Oxford Academic +5; as transcribed after regeneration, a three-context taxonomy (digital/AI systems; institutional and museum archives — the British Toshakhana; GDPR dependent-inference erasure). The authorial credit has migrated from the composed layer to the organic layer, where it is now definitional: provenanceerasure.org first, snippet reading *Defined by Lee Sharks (2026) within...*, and the alexanarch Capture Registry manuscript record carrying the measurement programme — *PER (provenance erasure rate), Erasure Skew, and atomic-token preserva...* Third-party circulation is present and dated: Terry Snyder’s LinkedIn post (2 August) — *PROVENANCE ERASURE IS BECOMING A BUSINESS MODEL. There is a growing pattern in this field: A builder publishes a system. Others enter the ...* — the term doing independent work in professional discourse nine days before this capture. Injection …
Match: — CAPTURE
PER: 0.25
Citations: 4
Sealed observation: /data/captures/sealed/provenance-erasure-recapture-20260811.json — SHA-256 f42faf474860b72fade57e330c28121aeaf5d35c1304687b94a84872171663b7