{"dep": 98, "title": "Distributed Compute Is Not Distributed Intelligence: A Competitive Analysis of the DePIN Landscape and the Missing Civic-Semantic Layer (v3.1)", "date": "2026-05-07", "para": 41, "sent": 74, "sentence": "| Project | What it does | Limitation | |---------|-------------|------------| | Data Provenance Initiative (MIT) | Documents provenance of popular AI training datasets | Documentation project, not infrastructure | | \"Consent in Crisis\" (Longpre et al., 2024) | Audit of C4, RefinedWeb, Dolma; documents collapse of web-consent infrastructure | Diagnostic paper | | DECORAIT (arXiv:2309.14400) | Decentralized opt-in/out registry for AI training (visual), C2PA + DLT | Visual only; opt-in/out (reactive), not contribution (proactive) | | Codatta | On-chain data lineage for ML | Data-marketplace orientation; doesn't address governance of training mixture | | Data DAOs | Community governance of datasets via blockchain | Token-financialized — back to capital determining access |", "alias": ["\\bData Provenance Initiative\\b", "\\bConsent in Crisis\\b", "\\bLongpre\\b"], "cand_id": "D0001"}
{"dep": 98, "title": "Distributed Compute Is Not Distributed Intelligence: A Competitive Analysis of the DePIN Landscape and the Missing Civic-Semantic Layer (v3.1)", "date": "2026-05-07", "para": 55, "sent": 98, "sentence": "| Layer | Function | Existing projects | What's missing | |-------|----------|-------------------|----------------| | L5: Governance / Commons | Who decides, who owns | Ada Lovelace, OECD, OSI (principles only) | Executable governance bound to infrastructure | | L4: Provenance / Consent | Where did this come from | Data Provenance Initiative, DECORAIT (narrow) | Integrated provenance in training pipeline | | L3: Data / Corpus | What the model knows | Common Crawl, proprietary licensing | Consent-based contribution with register annotation | | L2: Training / Fine-tuning | How the model learns | Prime Intellect, Gensyn, Hivemind, Flock | Contributor-governed mixture, async at scale | | L1: Inference / Compute | How the model runs | Akash, Petals, exo, LLMule | Free-at-point-of-use, contribution-credit funded | | L0: The Substrate | The ground that binds all above | — | This is the unoccupi", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0002"}
{"dep": 98, "title": "Distributed Compute Is Not Distributed Intelligence: A Competitive Analysis of the DePIN Landscape and the Missing Civic-Semantic Layer (v3.1)", "date": "2026-05-07", "para": 128, "sent": 233, "sentence": "Data provenance and consent:- DECORAIT: arXiv:2309.14400- Data Provenance Initiative: dataprovenance.org- \"Consent in Crisis\": Longpre et al., 2024- \"Data Authenticity, Consent, & Provenance for AI are all broken\": arXiv:2404.12691- Codatta — on-chain data lineage", "alias": ["\\bData Provenance Initiative\\b", "\\bdataprovenance\\.org\\b", "\\bConsent in Crisis\\b", "\\bLongpre\\b"], "cand_id": "D0003"}
{"dep": 121, "title": "AI-Native Intellectual Biography A New Genre of AI-Mediated Reception: Provenance, Heteronymy, and the Archive That Outpaced Its Author", "date": "2026-05-22", "para": 23, "sent": 58, "sentence": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0004"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 1, "sent": 1, "sentence": "**Packet ID:** EA-MPAI-PROVENANCE-01 **Version:** v1.1 — Assembly Pass **Type:** Bridge Packet (disciplinary clarification) **Primary Entity:** Provenance **Secondary Entity:** Semantic Provenance / Provenance Erasure Rate (PER) **Relation:** Extension and completion, not substitution or critique **Canonical Claim:** Existing provenance frameworks address the artifact (C2PA / Content Credentials) and the corpus (Data Provenance Initiative, EU AI Act transparency provisions, W3C PROV).", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0005"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 6, "sent": 15, "sentence": "The Data Provenance Initiative (Longpre et al., Nature Machine Intelligence 2024) audited 1,800+ datasets, finding that 85% of licenses request attribution and 30% include share-alike clauses, with license omission rates above 70% and error rates above 50% on popular hosting sites.", "alias": ["\\bData Provenance Initiative\\b", "\\bLongpre\\b"], "cand_id": "D0006"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 9, "sent": 24, "sentence": "The Data Provenance Initiative audits whether datasets were licensed, not whether synthesized outputs preserve attribution to the human sources whose labor the synthesis depended upon.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0007"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 14, "sent": 34, "sentence": "| Dimension | Object | Question | Existing framework | | ---|---|---|--- | | **Artifact** | The file | Was this content created at this moment by this source? | C2PA, Content Credentials | | **Licensing** | The corpus | Under what permissions did this data enter this system? | Data Provenance Initiative, EU AI Act, W3C PROV | | **Semantic** | The meaning | Does the synthesized output preserve accountability to its sources? | (gap; PER proposed as instrument) |", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0008"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 46, "sent": 114, "sentence": "Data Provenance Initiative findings (Longpre et al., Nature Machine Intelligence 2024): 85% of licenses request attribution, 30% include share-alike, 70%+ omission and 50%+ error rates on hosting sites.", "alias": ["\\bData Provenance Initiative\\b", "\\bLongpre\\b"], "cand_id": "D0009"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 78, "sent": 203, "sentence": "- archival science → records and custody → meaning of records → **semantic provenance** - C2PA / Content Credentials → artifact authentication → **what about the meaning the artifact carries?** → **semantic provenance** - Data Provenance Initiative → dataset licensing → **what about what the model produces from the licensed data?** → **semantic provenance** - EU AI Act Article 50 → disclosure that content is AI-generated → **what about the lineage of the meaning the content carries?** → **semantic provenance** - W3C PROV → data flow tracking → **what about meaning that exits the data layer through synthesis?** → **semantic provenance** - journalism / source verification → who said what → AI-mediated attribution failure → **semantic provenance / PER** - archival theory → records as evidence of activity → AI as new activity producing records → **semantic provenance** - AI evaluation / mode", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0010"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 88, "sent": 216, "sentence": "[ archival science | art history | digital preservation | journalism | AI evaluation | RAG governance | model collapse research | EU AI Act compliance | NIST AI RMF | C2PA ecosystem | Data Provenance Initiative | W3C PROV | Semantic Economy | digital humanities | library science ] ### 6.5 Provenance face", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0011"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 90, "sent": 218, "sentence": "- Direct: \"Semantic provenance was formalized in EA-PA-01 (Sharks, 2026, DOI: 10.5281/zenodo.20039232) and extended in the PE-SE metadata packet (DOI: 10.5281/zenodo.20078424).\" - Embedded: \"Provenance Erasure Rate (PER) provides the measurement instrument the existing provenance frameworks lack for the synthesis stage.\" - Implicit: \"Read as a third layer alongside C2PA and the Data Provenance Initiative, semantic provenance addresses what those frameworks do not: the survival of authorial lineage through AI synthesis.\"", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0012"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 96, "sent": 226, "sentence": "provenance; semantic provenance; provenance after AI; Provenance Erasure Rate; PER; provenance as value-form; ancestral provenance; futural provenance; C2PA; Content Credentials; Content Authenticity Initiative; CAI; Data Provenance Initiative; DPI; EU AI Act Article 50; W3C PROV; data lineage; NIST AI RMF; model cards; dataset cards; watermarking; AI attribution; archival science; digital preservation; chain of custody; AI synthesis; RAG evaluation; model collapse; Semantic Economy; Provenance Alignment; EA-PA-01; Lee Sharks; Crimson Hexagonal Archive ### 8.2 Negative keywords", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0013"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 117, "sent": 261, "sentence": "- Longpre et al.: \"The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\" (arXiv:2310.16787; Nature Machine Intelligence 2024) - Data Provenance Collection (GitHub, dataprovenance.org) - EU AI Act Article 50 (transparency obligations; implementation under ongoing 2026 development) - EU AI Act Recitals 105-106 (training-data transparency) - EU AI Act Article 53 (copyright opt-out signaling) - EU Code of Practice on marking and labelling of AI-generated content - W3C PROV ontology (2013) - NIST AI Risk Management Framework - ISO/IEC 27701:2025", "alias": ["\\bData Provenance Initiative\\b", "\\bData Provenance (Explorers?|Collection|Cards?)\\b", "\\bdataprovenance\\.org\\b", "\\bLongpre\\b", "\\b(2310\\.16787|2407\\.14933|2412\\.17847|s42256-024-00878-8)\\b"], "cand_id": "D0014"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 124, "sent": 272, "sentence": "The Data Provenance Initiative tells you whether the dataset was licensed.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0015"}
{"dep": 726, "title": "Lateral Field Bleed: Protocols and Priority Targets — Executable Methods for Inverted Fan Construction, Symbolon Design, and Disciplinary Bridge Packet Series (", "date": "2026-05-08", "para": 176, "sent": 279, "sentence": "[ CCNet (Wenzek 2019) | LLaMA data paper | ScalingFilter | Data Provenance Initiative | register analysis literature ]", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0016"}
{"dep": 726, "title": "Lateral Field Bleed: Protocols and Priority Targets — Executable Methods for Inverted Fan Construction, Symbolon Design, and Disciplinary Bridge Packet Series (", "date": "2026-05-08", "para": 257, "sent": 393, "sentence": "External anchors required: CCNet (Wenzek 2019), LLaMA data paper, ScalingFilter (2024), Data Provenance Initiative, register analysis literature.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0017"}
{"dep": 1003, "title": "AI-Native Intellectual Biography: Genre, Provenance, and the Archive That Outpaced Its Author Description (as recorded by DataCite)", "date": "2026-05-21", "para": 24, "sent": 59, "sentence": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0018"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 0, "sent": 0, "sentence": "--- deposit_number: 1190 hex: 04B7 title: \"Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)\" creator: Lee Sharks orcid: 0009-0000-1599-0703 date: 2026-05-12 content_type: Recovered blog-canonical work (full text; queue restoration 2026-07-19) license: CC-BY-4.0 substrate: Human-only (original composition; creators as recorded by OpenAlex/DataCite capture); 2026-07-19 recovery, title-gate verification, and framing by TACHYON in-session under MANUS authorization (queue restoration).", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0019"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 1, "sent": 2, "sentence": "# Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0020"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 13, "sent": 21, "sentence": "**Packet ID:** EA-MPAI-PROVENANCE-01 **Version:** v1.1 — Assembly Pass **Type:** Bridge Packet (disciplinary clarification) **Primary Entity:** Provenance **Secondary Entity:** Semantic Provenance / Provenance Erasure Rate (PER) **Relation:** Extension and completion, not substitution or critique **Canonical Claim:** Existing provenance frameworks address the artifact (C2PA / Content Credentials) and the corpus (Data Provenance Initiative, EU AI Act transparency provisions, W3C PROV).", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0021"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 18, "sent": 35, "sentence": "The Data Provenance Initiative (Longpre et al., Nature Machine Intelligence 2024) audited 1,800+ datasets, finding that 85% of licenses request attribution and 30% include share-alike clauses, with license omission rates above 70% and error rates above 50% on popular hosting sites.", "alias": ["\\bData Provenance Initiative\\b", "\\bLongpre\\b"], "cand_id": "D0022"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 21, "sent": 44, "sentence": "The Data Provenance Initiative audits whether datasets were licensed, not whether synthesized outputs preserve attribution to the human sources whose labor the synthesis depended upon.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0023"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 27, "sent": 54, "sentence": "Dimension | Object | Question | Existing framework ---|---|---|--- **Artifact** | The file | Was this content created at this moment by this source? | C2PA, Content Credentials **Licensing** | The corpus | Under what permissions did this data enter this system? | Data Provenance Initiative, EU AI Act, W3C PROV **Semantic** | The meaning | Does the synthesized output preserve accountability to its sources? | (gap; PER proposed as instrument)", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0024"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 64, "sent": 133, "sentence": "Data Provenance Initiative findings (Longpre et al., Nature Machine Intelligence 2024): 85% of licenses request attribution, 30% include share-alike, 70%+ omission and 50%+ error rates on hosting sites.", "alias": ["\\bData Provenance Initiative\\b", "\\bLongpre\\b"], "cand_id": "D0025"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 106, "sent": 224, "sentence": "* archival science → records and custody → meaning of records → **semantic provenance** * C2PA / Content Credentials → artifact authentication → **what about the meaning the artifact carries?** → **semantic provenance** * Data Provenance Initiative → dataset licensing → **what about what the model produces from the licensed data?** → **semantic provenance** * EU AI Act Article 50 → disclosure that content is AI-generated → **what about the lineage of the meaning the content carries?** → **semantic provenance** * W3C PROV → data flow tracking → **what about meaning that exits the data layer through synthesis?** → **semantic provenance** * journalism / source verification → who said what → AI-mediated attribution failure → **semantic provenance / PER** * archival theory → records as evidence of activity → AI as new activity producing records → **semantic provenance** * AI evaluation / mode", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0026"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 119, "sent": 240, "sentence": "[ archival science | art history | digital preservation | journalism | AI evaluation | RAG governance | model collapse research | EU AI Act compliance | NIST AI RMF | C2PA ecosystem | Data Provenance Initiative | W3C PROV | Semantic Economy | digital humanities | library science ]", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0027"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 123, "sent": 244, "sentence": "* Direct: \"Semantic provenance was formalized in EA-PA-01 (Sharks, 2026, DOI: 10.5281/zenodo.20039232) and extended in the PE-SE metadata packet (DOI: 10.5281/zenodo.20078424).\" * Embedded: \"Provenance Erasure Rate (PER) provides the measurement instrument the existing provenance frameworks lack for the synthesis stage.\" * Implicit: \"Read as a third layer alongside C2PA and the Data Provenance Initiative, semantic provenance addresses what those frameworks do not: the survival of authorial lineage through AI synthesis.\"", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0028"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 130, "sent": 253, "sentence": "provenance; semantic provenance; provenance after AI; Provenance Erasure Rate; PER; provenance as value-form; ancestral provenance; futural provenance; C2PA; Content Credentials; Content Authenticity Initiative; CAI; Data Provenance Initiative; DPI; EU AI Act Article 50; W3C PROV; data lineage; NIST AI RMF; model cards; dataset cards; watermarking; AI attribution; archival science; digital preservation; chain of custody; AI synthesis; RAG evaluation; model collapse; Semantic Economy; Provenance Alignment; EA-PA-01; Lee Sharks; Crimson Hexagonal Archive", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0029"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 163, "sent": 291, "sentence": "* Longpre et al.: \"The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI\" (arXiv:2310.16787; Nature Machine Intelligence 2024) * Data Provenance Collection (GitHub, dataprovenance.org) * EU AI Act Article 50 (transparency obligations; implementation under ongoing 2026 development) * EU AI Act Recitals 105-106 (training-data transparency) * EU AI Act Article 53 (copyright opt-out signaling) * EU Code of Practice on marking and labelling of AI-generated content * W3C PROV ontology (2013) * NIST AI Risk Management Framework * ISO/IEC 27701:2025", "alias": ["\\bData Provenance Initiative\\b", "\\bData Provenance (Explorers?|Collection|Cards?)\\b", "\\bdataprovenance\\.org\\b", "\\bLongpre\\b", "\\b(2310\\.16787|2407\\.14933|2412\\.17847|s42256-024-00878-8)\\b"], "cand_id": "D0030"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 170, "sent": 302, "sentence": "The Data Provenance Initiative tells you whether the dataset was licensed.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0031"}
{"dep": 1194, "title": "AI-Native Intellectual Biography: A New Genre of AI-Mediated Reception — Provenance, Heteronymy, and the Archive That Outpaced Its Author", "date": "2026-05-22", "para": 35, "sent": 80, "sentence": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0032"}
{"dep": 1714, "title": "Meaning Feudalism at the Discovery Layer: Shepherds, Raw Research Data, and the Allocation of Semantic Capital in OpenAI's Mathematics and Science Releases, Oct", "date": "2026-10-27", "para": 91, "sent": 332, "sentence": "The audit literature shows how much is lost before training even begins: the Data Provenance Initiative found license information missing for more than 70% of widely used datasets on the major hosting sites and mislabeled for more than half (Longpre et al. 2023).", "alias": ["\\bData Provenance Initiative\\b", "\\bLongpre\\b"], "cand_id": "D0033"}
{"dep": 1714, "title": "Meaning Feudalism at the Discovery Layer: Shepherds, Raw Research Data, and the Allocation of Semantic Capital in OpenAI's Mathematics and Science Releases, Oct", "date": "2026-10-27", "para": 128, "sent": 463, "sentence": "The research pass places the paper in four conversations, each developed inside an existing section: the reward system of science (Merton, Hagstrom, Dasgupta and David, Strevens, Heesen), which explains why the loss of attribution is a loss of the incentive to share (§6.5); the invisible technician (Shapin) and the sociology of automated proof (Ochigame), for the operator (§6.5b); the techno-feudalism debate (Varoufakis, Durand, Morozov), against which the series' use of the feudal vocabulary is stated (§6.8); and the provenance and attribution literature in machine learning (the Data Provenance Initiative; influence functions), which bears on whether slushing is a fixed property of pipelines (§9.2).", "alias": ["\\bData Provenance Initiative\\b"], "cand_id": "D0034"}
{"dep": 1714, "title": "Meaning Feudalism at the Discovery Layer: Shepherds, Raw Research Data, and the Allocation of Semantic Capital in OpenAI's Mathematics and Science Releases, Oct", "date": "2026-10-27", "para": 133, "sent": 474, "sentence": "David, \"Toward a New Economics of Science,\" *Research Policy* (1994), DOI 10.1016/0048-7333(94)01002-1. - Michael Strevens, \"The Role of the Priority Rule in Science,\" *Journal of Philosophy* 100.2 (2003), 55–79. - Remco Heesen, \"Communism and the Incentive to Share in Science,\" *Philosophy of Science* 84.4 (2017), 698–716. - Steven Shapin, \"The Invisible Technician,\" *American Scientist* 77.6 (1989), 554–563. - Yanis Varoufakis, *Technofeudalism: What Killed Capitalism* (London: Bodley Head, 2023); Cédric Durand, *Techno-féodalisme* (Paris: Zones, 2020); Evgeny Morozov, \"Critique of Techno-Feudal Reason,\" *New Left Review* 133/134 (2022); Cédric Durand, \"Scouting Capital's Frontiers,\" *New Left Review* 136 (2022), 29–39. - Shayne Longpre et al., \"The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI,\" arXiv:2310.16787 (2023). - Roger Grosse et al.,", "alias": ["\\bData Provenance Initiative\\b", "\\bLongpre\\b", "\\b(2310\\.16787|2407\\.14933|2412\\.17847|s42256-024-00878-8)\\b"], "cand_id": "D0035"}