Add mypy type checking to improve data pipeline reliability

The current codebase lacks static type checking, creating critical risks for:

• Data pipeline integrity: Type errors in fetch scripts corrupt downstream processing
• API integration reliability: External APIs return unstructured license formats requiring type validation.
• Silent failures: Runtime type errors go undetected until data analysis phase

## Example of some of the common CC license formats returned by the arXiv API response

```bash
# arXiv license format variations that cause runtime failures:
license_examples = [
"http://creativecommons.org/licenses/by/4.0/", # Full URL
"CC BY 4.0", # Short form
"Creative Commons Attribution 4.0 International", # Full name
"cc-by-4.0", # Lowercase with hyphens
"", # Empty string
None, # None value
["CC BY 4.0", "http://creativecommons.org/licenses/by/4.0/"] # List format
]
```
### A CASE FOR THE ADOPTION OF MYPY AS TYPE CHECKER

## Project Links:
• **Project Repository**: https://github.com/creativecommons/quantifying
• **Creative Commons Python Guidelines**: https://opensource.creativecommons.org/
• **mypy Documentation**: https://mypy.readthedocs.io/
• **mypy GitHub**: https://github.com/python/mypy
• **Type Checking PEP 484**: https://peps.python.org/pep-0484/

## Why mypy Over Alternatives like pyright, pyre, pytype

## mypy vs pyright:
• **Zero Dependencies**: mypy is pure Python; pyright requires 200MB+ Node.js runtime
• **CI Efficiency**: Native Python integration vs additional Node.js setup in GitHub Actions
• **Error Quality**: mypy provides actionable messages for data pipeline debugging
• **Library Ecosystem**: Superior third-party stub support for pandas/requests/matplotlib which are already adopted in the project

## mypy vs pyre:
• **Active Development**: mypy has 50+ contributors; pyre development stalled (last major release 18+ months)
• **Incremental Analysis**: mypy supports file-by-file checking; pyre requires full project analysis
• **Scientific Python**: Better numpy/pandas type support crucial for data processing

## mypy vs pytype:
• **Explicit Contracts**: mypy requires explicit annotations documenting API expectations; pytype's inference misses contract violations
• **Error Detection**: mypy catches 60% more type errors in data transformation code
• **Union Type Support**: Superior handling of multiple license format variations

### Project-Specific Advantages

Quantifying Commons Integration:
• **Seamless Toolchain**: Integrates with existing black/flake8/isort workflow already adopted in the project
• **License Normalization**: Strict typing prevents license format corruption in `normalizing_license_text()`
• **API Reliability**: Optional/Union types handle inconsistent arXiv/GitHub API responses
• **Data Integrity**: Catches type mismatches before they corrupt quarterly reports

Development Workflow:
• **Gradual Adoption**: Start with critical functions, expand incrementally
• **Configuration Consistency**: Uses mypy.ini following project's tool-specific config pattern
• **Python 3.11 Native**: Full compatibility with current project version

### Implementation Plan
1. Add `mypy` to Pipfile dev-packages
2. Create `mypy.ini` configuration file
3. Update `.pre-commit-config.yaml` with mypy hook
4. Add mypy to `.github/workflows/static_analysis.yml`
5. Type annotate `scripts/1-fetch/arxiv_fetch.py` in `normalizing_license_text()` function

### Acceptance Criteria
• [ ] mypy runs successfully on all Python files
• [ ] Pre-commit hooks include mypy validation
• [ ] GitHub Actions workflow includes mypy check
• [ ] Core data pipeline functions have type annotations
• [ ] Documentation updated with mypy usage instructions

Priority: Medium - Prevents data corruption in quarterly CC commons reports

## Implementation

- [x] I would be interested in implementing this feature.

Esamina Pipfile, mypy.ini, .pre-commit-config.yaml, .github/workflows/static_analysis.yml e scripts/1-fetch/arxiv_fetch.py, concentrandoti su normalizing_license_text(). Verifica come è configurato il workflow black/flake8/isort esistente, quindi implementa l’integrazione di mypy e le annotazioni indicate. Il lavoro è considerato completato quando mypy passa sui file Python, pre-commit e GitHub Actions lo eseguono, le funzioni principali della pipeline sono annotate e la documentazione sull’utilizzo è aggiornata.

Scritto dal modello di indicizzazione a partire dal testo della issue.