13. Peer Review Assistance

Peer review evaluates research quality, validity, and significance. Local AI supports reviewers in providing thorough, constructive assessments while helping authors understand reviewer feedback.

Review Draft Generation

Local models draft structured reviews from manuscript content:

# Peer review drafting assistance
def generate_review_draft(manuscript, evaluation_criteria):
    """Generate structured peer review from manuscript."""
    prompt = f"""Generate a structured peer review for this manuscript.
    Evaluate objectively against established criteria.
    Identify specific strengths and weaknesses with textual evidence.
    
    Evaluation Criteria:
    {evaluation_criteria}
    
    Manuscript Content:
    {manuscript}
    
    Provide assessment for: (1) Significance, (2) Originality, 
    (3) Technical Quality, (4) Clarity, (5) Reproducibility,
    (6) Ethical Compliance."""

    draft_review = local_model.generate(prompt, temperature=0.2)
    return draft_review

Constructive Feedback Generation

Helpful feedback identifies specific problems and suggests improvements:

def generate_constructive_feedback(weakness, context):
    """Generate actionable improvement suggestions."""
    prompt = f"""This weakness was identified in the manuscript:
    
    Weakness: {weakness}
    Context: {context}
    
    Generate specific, actionable suggestions for addressing 
    this weakness. Frame feedback constructively.
    If additional information or analysis is needed, specify 
    exactly what would be required."""
    
    suggestions = local_model.generate(prompt)
    return suggestions

Assessing Methodological Rigor

Local AI helps evaluate technical validity:

def assess_methodological_quality(methods_section, field_standards):
    """Evaluate methodological rigor against field standards."""
    prompt = f"""Assess the methodological quality of this research:
    
    Methods: {methods_section}
    Field Standards: {field_standards}
    
    Evaluate: sample size adequacy, control procedures, 
    measurement validity, analysis appropriateness, 
    and statistical power. Identify specific concerns 
    with evidence from the text."""
    
    assessment = local_model.generate(prompt)
    return assessment

Review Summary Generation

Help authors understand key points from multiple reviewer comments:

def summarize_reviewer_comments(reviews):
    """Generate consolidated summary of reviewer feedback."""
    prompt = f"""Summarize these reviewer comments into key themes.
    Distinguish major concerns (required changes) from minor 
    suggestions (recommended improvements). Prioritize issues 
    affecting validity or interpretation.
    
    Reviews: {reviews}"""
    
    summary = local_model.generate(prompt)
    return summary