WEKID Engine

Abstract

Operationalizing the framework calibration using weighted model scoring engine that is domain agnostic.

Evidence base

James Madigan Judge, WEKID: A Hierarchical Epistemic Framework and Operational Model for Evaluating and Governing Enterprise Artificial Intelligence Outputs, version 1.1 (self-published manuscript, March 2026).
Stephen Robertson and Hugo Zaragoza, The Probabilistic Relevance Framework: BM25 and Beyond, Foundations and Trends in Information Retrieval, vol. 3, no. 4 (2009): 333–389.
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi, “BERTScore: Evaluating Text Generation with BERT,” in Proceedings of the International Conference on Learning Representations (ICLR), 2020.
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning, “A Large Annotated Corpus for Learning Natural Language Inference,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, 2015, 632–642.
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal, “FEVER: A Large-Scale Dataset for Fact Extraction and VERification,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), vol. 1 (Long Papers), 2018, 809–819.
Potsawee Manakul, Adian Liusie, and Mark J. F. Gales, “SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, 2023.
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar, “Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation,” in Proceedings of the International Conference on Learning Representations (ICLR), 2023.
Nils Reimers and Iryna Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, 2019, 3982–3992.
Shahul Es, Jithin James, Luis Espinosa-Anke, and Steven Schockaert, “RAGAs: Automated Evaluation of Retrieval Augmented Generation,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics, 2024, 150–158.
Bowman et al., “A Large Annotated Corpus for Learning Natural Language Inference” (see note 4).
Adina Williams, Nikita Nangia, and Samuel R. Bowman, “A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), vol. 1 (Long Papers), 2018, 1112–1122.
Malcolm Gladwell, Outliers: The Story of Success (New York: Little, Brown, and Company, 2008).
Anders Ericsson and Robert Pool, Peak: Secrets from the New Science of Expertise (Boston: Houghton Mifflin Harcourt, 2016).
Philip E. Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction (New York: Crown, 2015).
Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus, and Giroux, 2011).
Peter M. Senge, The Fifth Discipline: The Art and Practice of the Learning Organization (New York: Doubleday, 1990).
Aristotle, Nicomachean Ethics, trans. Terence Irwin (Indianapolis: Hackett Publishing, 1999).

Where this proposal stands

• Proposed · 2026-08-11 14:49:43
  Submitted by Jim Judge
• Triaged · 2026-08-11 14:59:58
• Working-group review · 2026-08-11 15:01:55
• Public comment · 2026-08-11 15:03:40
• Council decision · 2026-08-12 17:14:06
• Versioned release · 2026-08-12 17:14:10