What is the ScholarEval framework?

ScholarEval is a retrieval-augmented evaluation framework that assesses research ideas based on soundness (empirical validity) and contribution (advancement relative to prior research).

Can I use this for non-technical research papers?

Yes, while the framework is often used for data science and ML, it covers broad dimensions like problem formulation, writing quality, and logical flow applicable to various scholarly disciplines.

How does the scoring system work?

It uses a 5-point scale across multiple dimensions (Methodology, Literature Review, etc.) and includes a Python script to calculate weighted aggregate scores for a holistic assessment.

Does this skill replace human peer reviewers?

No, it is designed to assist and augment human evaluation by providing systematic feedback, ensuring consistency in criteria, and helping authors improve their work before formal submission.

Scholar Evaluation

Name: Scholar Evaluation
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Systematically evaluates research papers and scholarly work using the peer-reviewed ScholarEval framework for soundness and contribution.

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Scholar Evaluation provides a structured methodology for assessing the quality and rigor of academic research. Built on the ScholarEval framework, it allows Claude to analyze research papers across eight core dimensions, including problem formulation, methodology, and data analysis. It is particularly useful for researchers, students, and peer reviewers who need to benchmark research ideas against literature, identify critical gaps, or generate actionable feedback for improving scholarly drafts and ensuring publication readiness.

Características Principales

Context-aware evaluation tailored to specific academic disciplines and venues
Standardized scoring rubrics for methodology, data, and writing quality
Automated aggregate score calculation via integrated Python scripts
8 GitHub stars
Multi-dimensional assessment of research soundness and contribution
Structured feedback generation for peer reviews or manuscript refinement

Casos de Uso

Identifying gaps and assessing the comprehensiveness of literature reviews
Evaluating the rigor and validity of research methodologies in AI and ML papers
Reviewing academic paper drafts for quality and submission readiness

Acerca de

Características Principales

Context-aware evaluation tailored to specific academic disciplines and venues
Standardized scoring rubrics for methodology, data, and writing quality
Automated aggregate score calculation via integrated Python scripts
8 GitHub stars
Multi-dimensional assessment of research soundness and contribution
Structured feedback generation for peer reviews or manuscript refinement

Casos de Uso

Identifying gaps and assessing the comprehensiveness of literature reviews
Evaluating the rigor and validity of research methodologies in AI and ML papers
Reviewing academic paper drafts for quality and submission readiness