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scholar-evaluation

K-Dense-AI · Данни и AI

Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.

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Scholar Evaluation

Purpose

Provide developmental, evidence-traceable feedback on a scholarly work: paper, draft, protocol, literature synthesis, or research idea. Use qualitative judgment first. Optional scores only describe how submitted evidence maps to a predeclared bounded rubric.

This skill also audits whether a low-stakes assessment process documents its construct, provenance, rater quality, uncertainty, traceability, sensitivity, fairness, accessibility, privacy, and human governance.

Hard safety boundary

Never use this skill to automate, recommend, materially influence, or score:

  • hiring, promotion, or tenure;
  • admissions;
  • grants or other funding;
  • prizes, honors, or awards;
  • discipline, dismissal, or sanctions; or
  • any other high-impact personnel decision.

Never rank people. Never reduce a person to a composite score. Never infer ability, character, integrity, protected traits, future performance, or worth. A nominal human-in-the-loop does not remove this boundary.

If asked for a prohibited use, stop. Offer developmental comments on a scholarly work or a process-only audit that does not process applications, compare people, recommend an outcome, or advise a decision.

Do not issue publication-readiness, accept/reject, or “top-tier” judgments.

Read references/responsible_assessment.md before any organizational use.

ScholarEval status

The referenced ScholarEval project is an experimental literature-grounded research-idea evaluation framework, not validated psychometrics.

The verified primary record is Moussa et al., ScholarEval: Research Idea Evaluation Grounded in Literature, arXiv:2510.16234v2, revised 2026-02-28. It reports a retrieval-augmented soundness/contribution framework, a 117-idea four-discipline dataset, coverage experiments, and a user study.

Do not generalize those results to person assessment, consequential decisions, all disciplines, or this skill's rubric. No peer-reviewed publication status was verified during the dated review. See references/source_ledger.md.

Metric and prestige policy

Do not score or infer quality from:

  • Journal Impact Factor or other journal measures;
  • h-index, publication counts, or citation counts;
  • altmetrics or attention;
  • journal, conference, venue, institution, employer, or geographic prestige;
  • author affiliation, reputation, network, or career path.

The rubric validator rejects common proxy-measure criteria.

Целият SKILL.md в GitHub