Journal Matchmaker

Analyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise.

Use Cases

• Find the best-fit journal for a new manuscript
• Identify high-impact factor journals in specific research areas
• Compare journal scopes against paper content
• Discover domain-specific publication venues

Usage

python scripts/main.py --abstract "Your paper abstract text here" [--field "field_name"] [--min-if 5.0] [--count 5]

Parameters

Parameter	Type	Required	Default	Description
--abstract	str	Yes	-	Paper abstract text to analyze
--field	str	No	Auto-detect	Research field (e.g., "computer_science", "biology")
--min-if	float	No	0.0	Minimum impact factor threshold
--max-if	float	No	None	Maximum impact factor (optional)
--count	int	No	5	Number of recommendations to return
--format	str	No	table	Output format: table, json, markdown

Examples

# Basic usage
python scripts/main.py --abstract "This paper presents a novel deep learning approach..."

# Specify field and minimum impact factor
python scripts/main.py --abstract "abstract.txt" --field "ai" --min-if 10.0 --count 10

# Output as JSON for integration
python scripts/main.py --abstract "..." --format json

How It Works

1. Abstract Analysis: Extracts key terms, methodology, and research focus
2. Field Classification: Identifies the primary research domain
3. Journal Matching: Compares content against journal scopes and aims
4. Impact Factor Filtering: Applies IF constraints if specified
5. Ranking: Scores and ranks journals by relevance and impact

Technical Details

• Difficulty: Medium
• Approach: Keyword extraction + journal database matching
• Data Source: Journal metadata from references/journals.json
• Algorithm: TF-IDF + cosine similarity for scope matching

References

• references/journals.json - Journal database with impact factors and scopes
• references/fields.json - Research field classifications
• references/scoring_weights.json - Algorithm tuning parameters

Notes

• Journal database should be updated periodically (quarterly recommended)
• Impact factor data sourced from Journal Citation Reports (JCR)
• Scope descriptions parsed from official journal websites
• For emerging fields, manual curation may be needed

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support