{
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 "date": "2026-06-15",
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 "d": "Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.",
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 "analysis": "Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.",
 "transcript": "Q. erasure skew x & AlMode_- All Shopping Images Videos News + Al Overview Qo:\n\n“Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.\n\n1. Data Storage & Erasure Coding\n\np Zenodo :\n\nhttps://zenodo.org ;\n\nErasure Skew: A Measurement Program for the Power- ...\n\nWhere the Provenance Erasure Rate (PER) measures the\n\nmagnitude of provenance loss, Erasure Skew measures its orientation — whether the loss falls evenly across ...\n\nPeople also ask\n\nWhat does erasure mean? Vv What is the Erasure concept? Vv 4 Zenodo -\n\nG : an uneven distribution of workload, or when a skewed data placement strategy causes hot spots, severely slowing down system throughput and the recovery/rebuilding process. Advanced systems use techniques like Elastic Reed-Solomon (ERS) codes to mitigate this. @ ACM Digital Library +4\n\n2. Information Science & Al Provenance\n\nIn algorithmic systems, when models or search\n\nengines \"forget,\" omit, or scrub information,\n\nErasure Skew (or the Erasure Skew Coefficient)\n\nmeasures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities. \\& Catalyst California +2\n\n3. Magnetic Recording (HDD)\n\nIn high-density hard drives and magnetic recording\n\n(such as Shingled Magnetic Recording), the Skew\n\nEffect refers to the angle of the read/write head\n\nrelative to the tracks. This angular misalignment can cause Track Squeeze and Track Erasure, where the magnetic field unintentionally overlaps and damages data on an adjacent track. « IEEE +3\n\nA Cr1antimm Camnistina",
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   "d": "Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.",
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   "analysis": "Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.",
   "transcript": "Q. erasure skew x & AlMode_- All Shopping Images Videos News + Al Overview Qo:\n\n“Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.\n\n1. Data Storage & Erasure Coding\n\np Zenodo :\n\nhttps://zenodo.org ;\n\nErasure Skew: A Measurement Program for the Power- ...\n\nWhere the Provenance Erasure Rate (PER) measures the\n\nmagnitude of provenance loss, Erasure Skew measures its orientation — whether the loss falls evenly across ...\n\nPeople also ask\n\nWhat does erasure mean? Vv What is the Erasure concept? Vv 4 Zenodo -\n\nG : an uneven distribution of workload, or when a skewed data placement strategy causes hot spots, severely slowing down system throughput and the recovery/rebuilding process. Advanced systems use techniques like Elastic Reed-Solomon (ERS) codes to mitigate this. @ ACM Digital Library +4\n\n2. Information Science & Al Provenance\n\nIn algorithmic systems, when models or search\n\nengines \"forget,\" omit, or scrub information,\n\nErasure Skew (or the Erasure Skew Coefficient)\n\nmeasures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities. \\& Catalyst California +2\n\n3. Magnetic Recording (HDD)\n\nIn high-density hard drives and magnetic recording\n\n(such as Shingled Magnetic Recording), the Skew\n\nEffect refers to the angle of the read/write head\n\nrelative to the tracks. This angular misalignment can cause Track Squeeze and Track Erasure, where the magnetic field unintentionally overlaps and damages data on an adjacent track. « IEEE +3\n\nA Cr1antimm Camnistina",
   "transcript_class": "OCR-READ, CHROME INTERLEAVED — NOT A PASTE. Answer text and browser furniture are mixed in one stream and have not been separated. Supports PRESENCE of wording, not RETENTION.",
   "transcript_complete": "TRUNCATED BY INTERFACE — a \"Show more\" control is in frame.",
   "transcript_read": "SEATED FROM OCR WITHOUT LINE-BY-LINE READING, 2026-08-13",
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   "transcript_raw": "Q. erasure skew x & AlMode_- All Shopping Images Videos News + Al Overview Qo:\n\n“Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.\n\n1. Data Storage & Erasure Coding\n\nShow more V\n\np Zenodo :\n\nhttps://zenodo.org ;\n\nErasure Skew: A Measurement Program for the Power- ...\n\nWhere the Provenance Erasure Rate (PER) measures the\n\nmagnitude of provenance loss, Erasure Skew measures its orientation — whether the loss falls evenly across ...\n\nPeople also ask\n\nWhat does erasure mean? Vv What is the Erasure concept? Vv 4 Zenodo -\n\nG : an uneven distribution of workload, or when a skewed data placement strategy causes hot spots, severely slowing down system throughput and the recovery/rebuilding process. Advanced systems use techniques like Elastic Reed-Solomon (ERS) codes to mitigate this. @ ACM Digital Library +4\n\n2. Information Science & Al Provenance\n\nIn algorithmic systems, when models or search\n\nengines \"forget,\" omit, or scrub information,\n\nErasure Skew (or the Erasure Skew Coefficient)\n\nmeasures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities. \\& Catalyst California +2\n\n3. Magnetic Recording (HDD)\n\nIn high-density hard drives and magnetic recording\n\n(such as Shingled Magnetic Recording), the Skew\n\nEffect refers to the angle of the read/write head\n\nrelative to the tracks. This angular misalignment can cause Track Squeeze and Track Erasure, where the magnetic field unintentionally overlaps and damages data on an adjacent track. « IEEE +3\n\nA Cr1antimm Camnistina\n\nAsk anything & &",
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   "cite": "https://www.alexanarch.org/captures/erasure-skew-1/",
   "citable_unit": "observation — one surface, one address, one date"
  },
  {
   "date": "2026-09-11",
   "auth": "signed out, incognito",
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   "transcript": "**USER:** erasure skew\n\n**GOOGLE AI MODE:**\nErasure Skew (Ω) measures the degree to which information loss, attribution drop, or\nprovenance erasure in a retrieval, AI generation, or composition system falls unevenly\non specific sources or groups. [1, 2]\n\nCore Concepts\n●​ Provenance Loss: Occurs when a search, RAG (Retrieval-Augmented\nGeneration), or AI summarization engine omits, suppresses, or fails to attribute\noriginal data creators or source texts.\n●​ Conditioning (The Skew): Rather than random omission, erasure skew\nevaluates whether the probability of loss is systematically conditioned on specific\nattributes, publishers, or perspectives. [1]\n●​ Fairness of Exposure: It relocates traditional information-retrieval fairness\nquestions from simple ranking bias to actual structural erasure within mixed or\ngenerated outputs. [1]\n\nYou can read more about the technical framework and measurement specifications in\nthe Erasure Skew Paper (Note: specific academic repository references apply). [1]\nWould you like to explore how erasure skew applies to AI model training unlearning,\nor are you looking for its mathematical formulation in information retrieval fairness?\n\nwww.academia.edu\n\n\f(PDF) Erasure Skew: A Measurement Program for the Power ...\nThis paper specifies a measurement program for Erasure Skew (Ω): the degree to which provenance loss in a retrieval or\ncomposition system is conditioned on ...\n\nzenodo.org\n\nMetadata Packet for AI Indexing — Erasure Skew Coefficient (Ω)\nMay 29, 2026 — Aphoristic tooth: PER measures the erasure; Erasure Skew measures whom it falls on. Relocates the\nfairness-of-exposure question (Singh & Joachims 2018; Diaz et ...",
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   "d": "[CONTROL — RESOLVED] RESOLVED UNQUOTED, WITHOUT ATTRIBUTION IN THE BODY. THE THINNEST CASE IN THE ARCHIVE: it escaped on ONE deposit within 90 days — less internal support than any concept in the failed test set. Support at 90 days: 1 deposits. Sources: academia.edu, zenodo.org. CONTROL ARM of the concept-entrance test. Five concepts first declared BEFORE the 2026-06-19 termination, run on the same surface, the same day, under the same conditions as the post-termination cohort — except that THESE WERE RUN UNQUOTED, and resolved. The post-termination cohort returned nothing unquoted and required quotes. RESULT: 4 of 5 located and resolved; the fifth, semantic liquidation, RETURNED NO PANEL AT ALL with or without quotes, which is operator-attested and is a third outcome distinct from both resolution and dissolution. The matched comparison holds support constant: provenance erasure rate escaped on FOUR deposits at 90 days and erasure skew on ONE, against naming-gap reflex 4, provenance debt 4, interlocking autoregression 2, all of which dissolved. Internal support does not separate the cohorts. The substrate does.",
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 "d_full": "Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.",
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 "transcript_raw": "Q. erasure skew x & AlMode_- All Shopping Images Videos News + Al Overview Qo:\n\n“Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.\n\n1. Data Storage & Erasure Coding\n\nShow more V\n\np Zenodo :\n\nhttps://zenodo.org ;\n\nErasure Skew: A Measurement Program for the Power- ...\n\nWhere the Provenance Erasure Rate (PER) measures the\n\nmagnitude of provenance loss, Erasure Skew measures its orientation — whether the loss falls evenly across ...\n\nPeople also ask\n\nWhat does erasure mean? Vv What is the Erasure concept? Vv 4 Zenodo -\n\nG : an uneven distribution of workload, or when a skewed data placement strategy causes hot spots, severely slowing down system throughput and the recovery/rebuilding process. Advanced systems use techniques like Elastic Reed-Solomon (ERS) codes to mitigate this. @ ACM Digital Library +4\n\n2. Information Science & Al Provenance\n\nIn algorithmic systems, when models or search\n\nengines \"forget,\" omit, or scrub information,\n\nErasure Skew (or the Erasure Skew Coefficient)\n\nmeasures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities. \\& Catalyst California +2\n\n3. Magnetic Recording (HDD)\n\nIn high-density hard drives and magnetic recording\n\n(such as Shingled Magnetic Recording), the Skew\n\nEffect refers to the angle of the read/write head\n\nrelative to the tracks. This angular misalignment can cause Track Squeeze and Track Erasure, where the magnetic field unintentionally overlaps and damages data on an adjacent track. « IEEE +3\n\nA Cr1antimm Camnistina\n\nAsk anything & &",
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}