Add reviewer expertise credential guard

reviewer-expertise-credential-guard/README.md

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+# Reviewer Expertise Credential Guard
+
+This module adds a focused community reputation guard for trusted reviewer expertise credentials.
+
+It evaluates whether a reviewer should receive weighted assignment credit or trusted-reviewer badge eligibility by checking declared expertise, evidence-backed domains, method credentials, evidence freshness, review history, conflicts of interest, and anonymous-review redaction.
+
+## Run
+
+```sh
+node reviewer-expertise-credential-guard/test.js
+node reviewer-expertise-credential-guard/demo.js
+```
+
+The demo writes JSON and Markdown reviewer artifacts to `reviewer-expertise-credential-guard/reports/`.
+
+## Review Surface
+
+The implementation is dependency-free, uses synthetic data only, and does not call external APIs or read credentials.

reviewer-expertise-credential-guard/acceptance-notes.md

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+# Acceptance Notes
+
+## Validation
+
+- `node reviewer-expertise-credential-guard/test.js`
+- `node reviewer-expertise-credential-guard/demo.js`
+- `node --check reviewer-expertise-credential-guard/index.js`
+- `node --check reviewer-expertise-credential-guard/test.js`
+- `node --check reviewer-expertise-credential-guard/demo.js`
+- `ffprobe -v error -show_entries format=duration,size -show_entries stream=codec_name,width,height -of default=noprint_wrappers=1 reviewer-expertise-credential-guard/demo.mp4`
+
+## Acceptance Coverage
+
+- Current domain and method evidence can make a reviewer trusted-reviewer eligible.
+- Missing domain evidence lowers review weight and requires steward review.
+- Conflicts block weighted assignment credit.
+- Stale credentials require refresh before trusted badge elevation.
+- Anonymous review mode redacts display name and ORCID from public profiles.
+- The output audit digest is deterministic for reviewer replay.

reviewer-expertise-credential-guard/demo.js

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+const fs = require("fs");
+const path = require("path");
+const { evaluateReviewerExpertiseCredentials } = require("./index");
+
+const outputDir = path.join(__dirname, "reports");
+fs.mkdirSync(outputDir, { recursive: true });
+
+const packet = {
+  now: "2026-06-01T12:00:00Z",
+  maxEvidenceAgeDays: 730,
+  minimumAcceptedReviews: 3,
+  reviewers: [
+    {
+      id: "reviewer-ada",
+      displayName: "Ada Reviewer",
+      orcid: "0000-0002-1825-0097",
+      institution: "open-science-lab",
+      declaredDomains: ["proteomics", "machine-learning"],
+      declaredMethods: ["bayesian-modeling", "mass-spectrometry"],
+      acceptedReviews: 16,
+      evidence: [
+        { type: "domain", domains: ["proteomics"], issuedAt: "2026-01-15T00:00:00Z", sourceId: "orcid-work-proteomics" },
+        { type: "domain", domains: ["machine-learning"], issuedAt: "2026-02-10T00:00:00Z", sourceId: "grant-ml" },
+        { type: "method", methods: ["bayesian-modeling"], issuedAt: "2026-03-01T00:00:00Z", sourceId: "review-method" },
+      ],
+      conflicts: [],
+    },
+    {
+      id: "reviewer-byron",
+      displayName: "Byron Reviewer",
+      institution: "northbridge-university",
+      declaredDomains: ["materials-science"],
+      declaredMethods: ["density-functional-theory"],
+      acceptedReviews: 2,
+      evidence: [{ type: "domain", domains: ["materials-science"], issuedAt: "2023-01-10T00:00:00Z", sourceId: "publication-old" }],
+      conflicts: [{ authorId: "author-lin", type: "recent-coauthor" }],
+    },
+  ],
+  assignments: [
+    {
+      id: "assign-protein",
+      manuscriptId: "ms-protein-forecast",
+      projectId: "project-protein",
+      reviewerId: "reviewer-ada",
+      requiredDomains: ["proteomics", "machine-learning"],
+      requiredMethods: ["bayesian-modeling"],
+      anonymousMode: true,
+      authorIds: ["author-lin"],
+      institutions: ["northbridge-university"],
+    },
+    {
+      id: "assign-materials",
+      manuscriptId: "ms-materials-benchmark",
+      projectId: "project-materials",
+      reviewerId: "reviewer-byron",
+      requiredDomains: ["materials-science"],
+      requiredMethods: ["electron-microscopy"],
+      anonymousMode: false,
+      authorIds: ["author-lin"],
+      institutions: ["northbridge-university"],
+    },
+  ],
+};
+
+const report = evaluateReviewerExpertiseCredentials(packet);
+const jsonPath = path.join(outputDir, "reviewer-expertise-credential-report.json");
+const markdownPath = path.join(outputDir, "reviewer-expertise-credential-report.md");
+
+fs.writeFileSync(jsonPath, JSON.stringify(report, null, 2));
+fs.writeFileSync(
+  markdownPath,
+  [
+    "# Reviewer Expertise Credential Guard Demo",
+    "",
+    `Decision: ${report.decision}`,
+    `Audit digest: ${report.auditDigest}`,
+    "",
+    "## Assignment Decisions",
+    "",
+    ...report.assignments.map(
+      (assignment) =>
+        `- ${assignment.assignmentId}: ${assignment.decision}; badge ${assignment.badge}; weight ${assignment.reviewWeight}`,
+    ),
+    "",
+    "## Findings",
+    "",
+    ...report.assignments.flatMap((assignment) =>
+      assignment.findings.map((finding) => `- ${assignment.assignmentId}: ${finding.severity} ${finding.code} - ${finding.message}`),
+    ),
+    "",
+    "## Public Profiles",
+    "",
+    ...report.assignments.map((assignment) => `- ${assignment.assignmentId}: ${JSON.stringify(assignment.publicProfile)}`),
+    "",
+  ].join("\n"),
+);
+
+console.log(`Wrote ${jsonPath}`);
+console.log(`Wrote ${markdownPath}`);
+console.log(`${report.decision}: ${report.counts.findings} finding(s), ${report.auditDigest}`);