feat(survey): add IETF landscape survey (kappa, phase0, rerate), gaps update; bump wimse-ect; gitignore run logs
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workspace/drafts/landscape-survey/references.bib
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workspace/drafts/landscape-survey/references.bib
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% References for the IETF AI/agent landscape survey.
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% Real, verifiable entries only. Agent-added entries must be checked.
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% ── IETF / Standards quantitative analysis ────────────────────────────────
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@inproceedings{mcquistin2021characterising,
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author = {McQuistin, Stephen and Karan, Mladen and Khare, Prashant and
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Perkins, Colin and Tyson, Gareth and Purver, Matthew and
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Healey, Patrick and Iqbal, Waleed and Qadir, Junaid and
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Castro, Ignacio},
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title = {Characterising the {IETF} Through the Lens of {RFC} Deployment},
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booktitle = {Proceedings of the 21st ACM Internet Measurement Conference
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({IMC} '21)},
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year = {2021},
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pages = {137--149},
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doi = {10.1145/3487552.3487821},
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isbn = {9781450391290},
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}
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@inproceedings{zhang2025affiliations,
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author = {Zhang, Yangjun and McQuistin, Stephen and Karan, Mladen and
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Ramirez-Centeno, Hugo Enrique and Perkins, Colin and
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Tyson, Gareth and Castro, Ignacio},
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title = {Two Decades of {IETF} Affiliations: Evolution and Impact},
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booktitle = {Proceedings of the 2025 Applied Networking Research Workshop
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({ANRW} '25)},
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year = {2025},
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doi = {10.1145/3744200.3744757},
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}
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@techreport{tenoever2022aid,
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author = {ten Oever, Niels and Cath, Corinne and K{\"u}hlewind, Mirja
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and Perkins, Colin S.},
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title = {Report from the {IAB} Workshop on Analyzing {IETF} Data
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({AID}) 2021},
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institution = {Internet Architecture Board},
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type = {RFC},
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number = {9307},
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year = {2022},
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month = sep,
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issn = {2070-1721},
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note = {Informational},
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doi = {10.17487/RFC9307},
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}
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@misc{jimenez2024automating,
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author = {Jim{\'e}nez, Jaime},
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title = {Automating {IETF} Insights Generation with {AI}},
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year = {2024},
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eprint = {2410.13301},
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archivePrefix = {arXiv},
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primaryClass = {cs.NI},
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}
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% ── LLM-assisted annotation / classification ──────────────────────────────
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@article{gilardi2023chatgpt,
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author = {Gilardi, Fabrizio and Alizadeh, Meysam and Kubli, Ma{\"e}l},
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title = {{ChatGPT} Outperforms Crowd-Workers for Text-Annotation Tasks},
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journal = {Proceedings of the National Academy of Sciences},
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year = {2023},
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volume = {120},
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number = {30},
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pages = {e2305016120},
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doi = {10.1073/pnas.2305016120},
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}
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@inproceedings{tan2024survey,
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author = {Tan, Zhen and Li, Dawei and Wang, Song and Beigi, Alimohammad
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and Jiang, Bohan and Bhattacharjee, Amrita and Karami,
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Mansooreh and Li, Jundong and Cheng, Lu and Liu, Huan},
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title = {Large Language Models for Data Annotation and Synthesis:
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{A} Survey},
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booktitle = {Proceedings of the 2024 Conference on Empirical Methods in
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Natural Language Processing ({EMNLP} 2024)},
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year = {2024},
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pages = {930--957},
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address = {Miami, Florida, USA},
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doi = {10.18653/v1/2024.emnlp-main.54},
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}
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@misc{yang2025agentprotocols,
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author = {Yang, Yingxuan and Chai, Huacan and Song, Yuanyi and
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Qi, Siyuan and Wen, Muning and Li, Ning and Liao, Junwei
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and Hu, Haoyi and Lin, Jianghao and Chang, Gaowei and
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Liu, Weiwen and Wen, Ying and Yu, Yong and Zhang, Weinan},
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title = {A Survey of {AI} Agent Protocols},
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year = {2025},
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eprint = {2504.16736},
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archivePrefix = {arXiv},
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primaryClass = {cs.AI},
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}
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% ── Inter-rater reliability of LLM labels ─────────────────────────────────
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@misc{reiss2023reliability,
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author = {Reiss, Michael V.},
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title = {Testing the Reliability of {ChatGPT} for Text Annotation
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and Classification: {A} Cautionary Remark},
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year = {2023},
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eprint = {2304.11085},
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archivePrefix = {arXiv},
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primaryClass = {cs.CL},
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}
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@inproceedings{wang2023nlgeval,
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author = {Wang, Jiaan and Liang, Yunlong and Meng, Fandong and
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Sun, Zengkui and Shi, Haoxiang and Li, Zhixu and Xu, Jinan
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and Qu, Jianfeng and Zhou, Jie},
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title = {Is {ChatGPT} a Good {NLG} Evaluator? {A} Preliminary Study},
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booktitle = {Proceedings of the 4th Workshop on New Frontiers in
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Summarization ({NewSumm@EMNLP} 2023)},
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year = {2023},
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note = {arXiv:2303.04048},
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}
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% ── Cohen's kappa / Landis-Koch (methodological) ──────────────────────────
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@article{cohen1960kappa,
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author = {Cohen, Jacob},
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title = {A Coefficient of Agreement for Nominal Scales},
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journal = {Educational and Psychological Measurement},
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year = {1960},
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volume = {20},
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number = {1},
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pages = {37--46},
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doi = {10.1177/001316446002000104},
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}
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@article{landis1977kappa,
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author = {Landis, J. Richard and Koch, Gary G.},
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title = {The Measurement of Observer Agreement for Categorical Data},
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journal = {Biometrics},
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year = {1977},
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volume = {33},
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number = {1},
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pages = {159--174},
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doi = {10.2307/2529310},
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}
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% ── Text embeddings / semantic similarity ─────────────────────────────────
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@inproceedings{reimers2019sbert,
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author = {Reimers, Nils and Gurevych, Iryna},
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title = {Sentence-{BERT}: Sentence Embeddings using Siamese {BERT}-Networks},
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booktitle = {Proceedings of the 2019 Conference on Empirical Methods in
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Natural Language Processing and the 9th International Joint
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Conference on Natural Language Processing
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({EMNLP-IJCNLP} 2019)},
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year = {2019},
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pages = {3982--3992},
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doi = {10.18653/v1/D19-1410},
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}
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@misc{nussbaum2024nomic,
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author = {Nussbaum, Zach and Morris, John X. and Duderstadt, Brandon
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and Mulyar, Andriy},
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title = {Nomic Embed: Training a Reproducible Long Context Text
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Embedder},
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year = {2024},
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eprint = {2402.01613},
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archivePrefix = {arXiv},
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primaryClass = {cs.CL},
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}
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