dvcrn 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
- Abstract Analysis: Extracts key terms, methodology, and research focus
- Field Classification: Identifies the primary research domain
- Journal Matching: Compares content against journal scopes and aims
- Impact Factor Filtering: Applies IF constraints if specified
- 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 scopesreferences/fields.json- Research field classificationsreferences/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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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