People's Evidence Lab Performance Plan Performance plan for advancing the purposes, values, stakeholders, intended results, and evidence-related interoperability relationships of People's Evidence Lab. This StratML Part 2 rendition has been derived by ChatGPT from the StratML Part 1 rendition of People's Evidence Lab, at https://stratml.us/docs/PEL2.xml, and extended with performance indicators and Relationship elements. ^^ The relationships to W3C Community and Business Groups are inferred from the W3C Community and Business Groups relationships map at https://stratml.us/docs/W3CCBG_Part2.xml. RelationshipType values follow the StratML Part 2 controlled vocabulary: Broader_Than, Narrower_Than, and Peer_To. Performance indicators are initial placeholders pending specification by People's Evidence Lab. ^^ Submitter's Note: The content of this Other Information element has been lightly edited in the form at https://stratml.us/forms/Claude/Part2.html People's Evidence Lab PEL b5576cb0-7923-4f67-bb49-60d579e77813 People's Evidence Lab develops methodologies, research, and tools intended to improve the trustworthiness, usability, contextual relevance, and operational adoption of evidence in healthcare, education, and artificial intelligence contexts. Stephen J. Watt Stephen J. Watt, MD, is the Founder and Principal of People's Evidence Lab. His work focuses on improving how evidence is translated, contextualized, trusted, and operationalized within healthcare, education, and AI-enabled systems. His professional background includes medical affairs, evidence strategy, digital health innovation, learning systems, and responsible AI applications. His research and leadership activities emphasize contextual evidence design, explainability, empathy measurement in large language models, and methodologies intended to bridge the gap between evidence generation and real-world decision-making. Leadership Guide the development, refinement, and application of PEL methodologies, research, and tools. Performer Healthcare Organizations Healthcare organizations seeking to improve the usability and trustworthiness of evidence for clinical and operational decisions. Application Apply trustworthy evidence methods in clinical, operational, formulary, and regulatory contexts. Life Science Organizations Life science organizations seeking to improve evidence generation, communication, and adoption. Evidence Generate, communicate, and use evidence in ways that improve trust, traceability, and decision usefulness. Educational Organizations Educational institutions and learning system leaders adopting AI-enabled technologies and evidence-informed practices. Learning Adopt evidence-informed and AI-enabled practices that can be explained, trusted, and improved. Medical Affairs Leaders Medical affairs and evidence generation leaders responsible for translating evidence into clinical and regulatory decisions. Translation Translate evidence into actionable knowledge for clinicians, regulators, payers, and other decision-makers. Performer Digital Health Teams Digital health product and data teams integrating trustworthy AI-enabled capabilities into healthcare systems. Implementation Implement evidence-informed and AI-enabled capabilities within digital health systems. Performer Payers Payers and health technology assessment bodies evaluating evidence for policy and reimbursement decisions. Evaluation Evaluate evidence for coverage, reimbursement, policy, and technology assessment decisions. Evidence Intermediaries Stakeholders responsible for translating, interpreting, and contextualizing evidence for decision-makers. Intermediation Communicate, contextualize, and preserve traceability of evidence for decision-makers. Evidence trusted and effectively applied across institutional, professional, technological, and social contexts. _2e766d6b-0a7d-4dab-9c21-019044ee91d0 To develop methodologies, research, and tools that improve the clarity, contextual fit, traceability, and practical usability of evidence for decision-makers in healthcare, education, and AI-enabled systems. c0104e68-1769-407c-995e-4bbfef8458e6 Clarity Ensure evidence is understandable and actionable for the people expected to use it. Contextualization Adapt evidence to the operational settings and conditions in which decisions are actually made. Traceability Enable users to understand where evidence originated and how it was constructed in order to calibrate trust appropriately. Trust Promote warranted trust in evidence, methodologies, and AI-generated outputs. Empathy Advance research into empathy measurement and human-centered AI interaction. Transparency Support transparent methodologies and explainable evidence generation processes. Methodologies Develop methodologies that improve the practical usability, trustworthiness, contextual relevance, and adoption of evidence. cf44f52f-d7ac-46e3-95b0-f6f45c49a733 1 Translation Improve how evidence is communicated, interpreted, and operationalized in real-world decision-making contexts. _8e3fa479-5606-4c1f-9801-ddbdad7259cb 1.1 1.1.1 Evidence Translation Methodology Status _pel-pi-1-1-1 _pel-rel-1-1-schema https://stratml.us/docs/W3CCBG_Part2.xml#ced85cce-007b-4875-8f15-96344d7dd142 Structured Evidence Vocabulary PEL Translation peer to W3C Schema.org: Evidence translation can be improved by structured vocabularies that make concepts, claims, sources, and contexts more consistently interpretable across systems. _pel-rel-1-1-jsonld https://stratml.us/docs/W3CCBG_Part2.xml#3aa94687-f066-42dd-ad2c-bd0242a81a5d Linked Data Translation PEL Translation peer to W3C JSON & Linked Data: JSON-LD provides a practical format for connecting human-readable evidence summaries with machine-readable context. Documented Evidence translation methodology documented with structured concepts, source references, and decision-context guidance. TBD To Be Determined Calibration Support warranted trust in evidence and AI outputs through transparent and traceable methodologies. _262d4f2b-a64d-4202-85e3-37ec54527030 1.2 1.2.1 Trust Calibration Status _pel-pi-1-2-1 _pel-rel-1-2-credentials https://stratml.us/docs/W3CCBG_Part2.xml#381e4edd-f1ee-49cf-86fc-aaae4a324fa2 Trust Assertion PEL Calibration peer to W3C Credentials: Digital credentials support creation, presentation, verification, and user control of trust-relevant claims, aligning with PEL's emphasis on warranted trust. _pel-rel-1-2-fair https://stratml.us/docs/W3CCBG_Part2.xml#0c882ff2-e5e9-497f-8c2a-b143bf7e1e58 Trustworthy Metadata PEL Calibration peer to W3C FAIR Data Point: FAIR metadata practices support discoverability, accessibility, interoperability, and reuse of evidence needed to calibrate trust. Documented Trust calibration criteria documented for evidence sources, AI outputs, and decision-support artifacts. TBD To Be Determined Contextualization Adapt evidence to the operational settings in which decisions occur rather than presenting it abstractly. _97afb36f-2657-4734-aaf1-c82958da1ecf 1.3 1.3.1 Evidence Contextualization Status _pel-pi-1-3-1 _pel-rel-1-3-userjourney https://stratml.us/docs/W3CCBG_Part2.xml#0088ea1b-e0da-49e1-a054-270667451910 Decision Journey Context PEL Contextualization peer to W3C User Journey Graph: Evidence contextualization benefits from machine-readable representations of user journeys, decision paths, and implementation alignment over time. _pel-rel-1-3-knowledgegraphs https://stratml.us/docs/W3CCBG_Part2.xml#b96efe79-8689-4548-bccd-0dc57d52b40c Context Graphs PEL Contextualization peer to W3C Knowledge Graphs: Knowledge graphs can represent relationships among evidence, actors, settings, interventions, outcomes, and assumptions. Documented Contextualization methodology documented for adapting evidence to decision settings and stakeholder use cases. TBD To Be Determined Research Conduct research supporting trustworthy and human-centered applications of evidence and artificial intelligence. _7f78c030-eec6-4263-9682-6a0241945230 2 Education Advance research into AI-enabled education systems and evidence-informed learning practices. f64ddc15-2a0c-4c92-99be-626213deb31e 2.1 2.1.1 Education Research Status _pel-pi-2-1-1 _pel-rel-2-1-userjourney https://stratml.us/docs/W3CCBG_Part2.xml#0088ea1b-e0da-49e1-a054-270667451910 Learning Journey Evidence PEL Education peer to W3C User Journey Graph: AI-enabled education research can benefit from explicit, machine-readable representations of learner, instructor, and institutional journeys. _pel-rel-2-1-webagents https://stratml.us/docs/W3CCBG_Part2.xml#26ed2ec6-293e-4089-acb3-5b7cd4121c12 Learning Agent Support PEL Education peer to W3C Web Agents: AI-enabled learning systems may involve agents acting on behalf of learners, instructors, and institutions within web environments. Documented Research agenda documented for evaluating evidence-informed and AI-enabled education systems. TBD To Be Determined Empathy Investigate empathy measurement and related behavioral characteristics in large language models. _777d92f3-a84a-4c6a-8040-e5d5f0447ebc 2.2 2.2.1 Empathy Measurement Status _pel-pi-2-2-1 _pel-rel-2-2-webagents https://stratml.us/docs/W3CCBG_Part2.xml#26ed2ec6-293e-4089-acb3-5b7cd4121c12 Human-Agent Interaction PEL Empathy peer to W3C Web Agents: Empathy measurement in language models is relevant to agents that interact with users and act on their behalf in web environments. _pel-rel-2-2-aiknowledge https://stratml.us/docs/W3CCBG_Part2.xml#2a41a8bf-1597-4552-9919-82455dd4eba2 Behavioral Representation PEL Empathy peer to W3C AI Knowledge Representation: Measuring empathy in AI systems requires explicit representation of behavioral constructs, contexts, evidence, and interpretive assumptions. Documented Empathy measurement approach documented for evaluating large language model behavior in relevant decision-support contexts. TBD To Be Determined Governance Support trustworthy AI governance through evidence-based methodologies and traceable decision-support practices. _88d77343-22ce-407d-a73e-d08f061b0dc7 2.3 2.3.1 AI Governance Research Status _pel-pi-2-3-1 _pel-rel-2-3-aiknowledge https://stratml.us/docs/W3CCBG_Part2.xml#2a41a8bf-1597-4552-9919-82455dd4eba2 Governance Knowledge Representation PEL Governance peer to W3C AI Knowledge Representation: Evidence-based AI governance depends upon explicit representation of claims, risks, controls, assumptions, and decision criteria. _pel-rel-2-3-credentials https://stratml.us/docs/W3CCBG_Part2.xml#381e4edd-f1ee-49cf-86fc-aaae4a324fa2 Governance Credentials PEL Governance peer to W3C Credentials: Verifiable claims and credentials can support governance accountability, qualification, provenance, and assurance practices. Documented AI governance methodology documented with evidence traceability, accountability, and decision-support criteria. TBD To Be Determined Adoption Enable organizations and decision-makers to operationalize trustworthy evidence and AI-supported systems. c4288269-31b5-422d-918c-77c655b41c44 3 Healthcare Support healthcare stakeholders in using evidence effectively within clinical, formulary, and regulatory contexts. _271a8eaa-d402-44d2-b934-0a4a1d670114 3.1 3.1.1 Healthcare Adoption Status _pel-pi-3-1-1 _pel-rel-3-1-fair https://stratml.us/docs/W3CCBG_Part2.xml#0c882ff2-e5e9-497f-8c2a-b143bf7e1e58 Healthcare Evidence Metadata PEL Healthcare peer to W3C FAIR Data Point: Healthcare evidence adoption depends upon metadata practices that support findability, accessibility, interoperability, and reuse. _pel-rel-3-1-entityreconciliation https://stratml.us/docs/W3CCBG_Part2.xml#58e334a9-e035-455f-9b6c-cf0554e93435 Evidence Entity Reconciliation PEL Healthcare peer to W3C Entity Reconciliation: Clinical, formulary, and regulatory evidence frequently require reconciliation of entities such as interventions, populations, outcomes, organizations, and identifiers. Documented Healthcare adoption approach documented for evidence use in clinical, formulary, and regulatory contexts. TBD To Be Determined Education Support educational leaders adopting AI systems with evidence they can trust and explain. _0ae7474c-6927-4374-b2e3-768558cfe011 3.2 3.2.1 Education Adoption Status _pel-pi-3-2-1 _pel-rel-3-2-credentials https://stratml.us/docs/W3CCBG_Part2.xml#381e4edd-f1ee-49cf-86fc-aaae4a324fa2 Education Trust Assertions PEL Education peer to W3C Credentials: Educational leaders adopting AI systems may need verifiable claims about model behavior, instructional alignment, data provenance, and assessment evidence. _pel-rel-3-2-schema https://stratml.us/docs/W3CCBG_Part2.xml#ced85cce-007b-4875-8f15-96344d7dd142 Educational Evidence Structure PEL Education peer to W3C Schema.org: Structured metadata can help educational stakeholders discover, compare, and explain evidence associated with AI-enabled learning systems. Documented Education adoption approach documented for trustworthy and explainable AI-enabled learning systems. TBD To Be Determined Intermediation Help evidence intermediaries communicate and contextualize evidence using traceable methodologies. _0d9a5c1e-c7b2-4e79-81a0-ea6db41a9c41 3.3 3.3.1 Evidence Intermediation Status _pel-pi-3-3-1 _pel-rel-3-3-hypermedia https://stratml.us/docs/W3CCBG_Part2.xml#4b729d72-5438-4edf-9f65-b3a9d3047644 Evidence Navigation PEL Intermediation peer to W3C Hypermedia: Evidence intermediaries need navigable, contextual interfaces that preserve links among claims, sources, assumptions, and decision contexts. _pel-rel-3-3-rdfdev https://stratml.us/docs/W3CCBG_Part2.xml#e9dc7ff6-b66c-4af9-83ad-700e2366bda1 Evidence Graph Development PEL Intermediation peer to W3C RDF-DEV: RDF development practices can support interoperable evidence graphs and reusable schemas for communicating contextualized evidence. Documented Evidence intermediation approach documented for communicating contextualized and traceable evidence to decision-makers. TBD To Be Determined 2026-05-13 https://www.peoplesevidencelab.com/about Owen Ambur Owen.Ambur@verizon.net